Remove wait

- Implements TurnAwareTranscriptProcessor that combines user and assistant transcript tracking with turn boundary detection
- Correctly handles interruptions by capturing only what was actually spoken
- Emits on_turn_started and on_turn_ended events with accumulated transcripts
- Handles async frame processing with strategic delays to ensure proper text accumulation
- Adds comprehensive tests covering basic flow, interruptions, and multiple turns
- Includes documentation and usage examples

.github/workflows/generate-changelog.yml

@@ -1,174 +0,0 @@
-name: Generate Changelog for Release
-
-on:
-  workflow_dispatch:
-    inputs:
-      version:
-        description: "Release version (e.g., 0.0.97)"
-        required: true
-        type: string
-      date:
-        description: "Release date (YYYY-MM-DD format, defaults to today)"
-        required: false
-        type: string
-        default: ""
-
-permissions:
-  contents: write
-  pull-requests: write
-
-jobs:
-  generate-changelog:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.12"
-
-      - name: Install uv
-        uses: astral-sh/setup-uv@v4
-        with:
-          enable-cache: true
-
-      - name: Install dependencies
-        run: |
-          uv sync --group dev
-
-      - name: Set release date
-        id: set_date
-        run: |
-          if [ -z "${{ inputs.date }}" ]; then
-            RELEASE_DATE=$(date +%Y-%m-%d)
-            echo "Using today's date: $RELEASE_DATE"
-          else
-            RELEASE_DATE="${{ inputs.date }}"
-            echo "Using provided date: $RELEASE_DATE"
-          fi
-          echo "release_date=$RELEASE_DATE" >> $GITHUB_OUTPUT
-
-      - name: Validate inputs
-        run: |
-          # Validate version format (basic check)
-          if ! [[ "${{ inputs.version }}" =~ ^[0-9]+\.[0-9]+\.[0-9]+.*$ ]]; then
-            echo "Error: Version must be in format X.Y.Z (e.g., 0.0.97)"
-            exit 1
-          fi
-
-          # Validate date format if provided
-          if [ -n "${{ inputs.date }}" ]; then
-            if ! date -d "${{ inputs.date }}" >/dev/null 2>&1; then
-              # Try macOS date format
-              if ! date -j -f "%Y-%m-%d" "${{ inputs.date }}" >/dev/null 2>&1; then
-                echo "Error: Date must be in YYYY-MM-DD format (e.g., 2025-12-04)"
-                exit 1
-              fi
-            fi
-          fi
-
-      - name: Check for changelog fragments
-        id: check_fragments
-        run: |
-          FRAGMENT_COUNT=$(find changelog -name "*.md" ! -name "_template.md.j2" | wc -l | tr -d ' ')
-          echo "fragment_count=$FRAGMENT_COUNT" >> $GITHUB_OUTPUT
-
-          if [ "$FRAGMENT_COUNT" -eq "0" ]; then
-            echo "❌ Error: No changelog fragments found in changelog/"
-            echo ""
-            echo "Cannot create a release without changelog entries."
-            echo "Add changelog fragments to the changelog/ directory (e.g., 1234.added.md) and try again."
-            exit 1
-          fi
-
-          # Validate fragment types
-          VALID_TYPES="added changed deprecated removed fixed security"
-          INVALID_FRAGMENTS=""
-
-          for file in changelog/*.md; do
-            # Skip template
-            if [[ "$file" == "changelog/_template.md.j2" ]]; then
-              continue
-            fi
-            
-            # Extract type from filename (e.g., 1234.added.md -> added)
-            filename=$(basename "$file")
-            # Handle both 1234.added.md and 1234.added.2.md patterns
-            type=$(echo "$filename" | sed -E 's/^[0-9]+\.([a-z]+)(\.[0-9]+)?\.md$/\1/')
-            
-            # Check if type is valid
-            if ! echo "$VALID_TYPES" | grep -wq "$type"; then
-              INVALID_FRAGMENTS="$INVALID_FRAGMENTS\n  - $filename (type: '$type')"
-            fi
-          done
-
-          if [ -n "$INVALID_FRAGMENTS" ]; then
-            echo "❌ Error: Invalid changelog fragment types found:"
-            echo -e "$INVALID_FRAGMENTS"
-            echo ""
-            echo "Valid types are: $VALID_TYPES"
-            echo "Example: 1234.added.md, 5678.fixed.md"
-            exit 1
-          fi
-
-          echo "✓ Found $FRAGMENT_COUNT changelog fragment(s)"
-          echo "has_fragments=true" >> $GITHUB_OUTPUT
-
-      - name: Preview changelog
-        run: |
-          echo "## Preview of changelog for version ${{ inputs.version }}"
-          echo ""
-          uv run towncrier build --draft --version "${{ inputs.version }}" --date "${{ steps.set_date.outputs.release_date }}"
-
-      - name: Build changelog
-        run: |
-          uv run towncrier build --version "${{ inputs.version }}" --date "${{ steps.set_date.outputs.release_date }}" --yes
-
-      - name: Create Pull Request
-        uses: peter-evans/create-pull-request@v7
-        with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          commit-message: "Update changelog for version ${{ inputs.version }}"
-          title: "Release ${{ inputs.version }} - Changelog Update"
-          body: |
-            ## Changelog Update for Release ${{ inputs.version }}
-
-            This PR updates the CHANGELOG.md with all changes for version **${{ inputs.version }}**.
-
-            ### Summary
-            - **Version:** ${{ inputs.version }}
-            - **Date:** ${{ steps.set_date.outputs.release_date }}
-            - **Fragments processed:** ${{ steps.check_fragments.outputs.fragment_count }}
-
-            ### What this PR does
-            - ✅ Adds new release section to CHANGELOG.md
-            - ✅ Removes processed changelog fragments
-            - ✅ Ready to merge for release
-
-            ### Next Steps
-            1. Review the changelog entries below
-            2. Make any necessary edits to CHANGELOG.md if needed
-            3. Merge this PR
-            4. Continue with your release process
-
-            ---
-
-            <details>
-            <summary>📋 Preview of changes</summary>
-
-            The changelog has been updated with entries from the following fragments:
-
-            ```bash
-            ${{ steps.check_fragments.outputs.fragment_count }} fragments processed
-            ```
-
-            </details>
-          branch: changelog-${{ inputs.version }}
-          delete-branch: true
-          labels: |
-            changelog
-            release

CHANGELOG.md

@@ -5,145 +5,10 @@ All notable changes to **Pipecat** will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-<!-- towncrier release notes start -->
-
-## [0.0.97] - 2025-12-05
+## [Unreleased]
 
 ### Added
 
-- Added new Gradium services, `GradiumSTTService` and `GradiumTTSService`, for
-  speech-to-text and text-to-speech functionality using Gradium's API.
-
-- Additions for `AsyncAITTSService` and `AsyncAIHttpTTSService`:
-
-  - Added new `languages`: `pt`, `nl`, `ar`, `ru`, `ro`, `ja`, `he`, `hy`,
-    `tr`, `hi`, `zh`.
-  - Updated the default model to `asyncflow_multilingual_v1.0` for improved
-    accuracy and broader language coverage.
-
-- Added optional tool and tool output filters for MCP services.
-
-### Changed
-
-- Updated Deepgram logging to include Deepgram request IDs for improved
-  debugging.
-
-- Text Aggregation Improvements:
-
-  - **Breaking Change**: `BaseTextAggregator.aggregate()` now returns
-    `AsyncIterator[Aggregation]` instead of `Optional[Aggregation]`. This
-    enables the aggregator to return multiple results based on the provided
-    text.
-  - Refactored text aggregators to use inheritance: `SkipTagsAggregator` and
-    `PatternPairAggregator` now inherit from `SimpleTextAggregator`, reusing
-    the base class's sentence detection logic.
-
-- Improved interruption handling to prevent bots from repeating themselves. LLM
-  services that return multiple sentences in a single response (e.g.,
-  `GoogleLLMService`) are now split into individual sentences before being sent
-  to TTS. This ensures interruptions occur at sentence boundaries, preventing
-  the bot from repeating content after being interrupted during long responses.
-
-- Updated `AICFilter` to use Quail STT as the default model
-  (`AICModelType.QUAIL_STT`). Quail STT is optimized for human-to-machine
-  interaction (e.g., voice agents, speech-to-text) and operates at a native
-  sample rate of 16 kHz with fixed enhancement parameters.
-
-- If an unexpected exception is caught, or if `FrameProcessor.push_error()` is
-  called with an exception, the file name and line number where the exception
-  occured are now logged.
-
-- Updated Smart Turn model weights to v3.1.
-
-- Smart Turn analyzer now uses the full context of the turn rather than just
-  the audio since VAD last triggered.
-
-- Updated `CartesiaSTTService` to return the full transcription `result` in the
-  `TranscriptionFrame` and `InterimTranscriptionFrame`. This provides access to
-  word timestamp data.
-
-- `HumeTTSService` changes:
-
-  - Added tracking headers (`X-Hume-Client-Name` and `X-Hume-Client-Version`)
-    to all requests made by `HumeTTSService` to the Hume API for better usage
-    tracking and analytics.
-  - Added `stop()` and `cancel()` cleanup methods to `HumeTTSService` to
-    properly close the HTTP client and prevent resource leaks.
-
-### Deprecated
-
-- NVIDIA Services name changes (all functionality is unchanged):
-
-  - `NimLLMService` is now deprecated, use `NvidiaLLMService` instead.
-  - `RivaSTTService` is now deprecated, use `NvidiaSTTService` instead.
-  - `RivaTTSService` is now deprecated, use `NvidiaTTSService` instead.
-  - Use `uv pip install pipecat-ai[nvidia]` instead of
-    `uv pip install pipecat-ai[riva]`
-
-- The `noise_gate_enable` parameter in `AICFilter` is deprecated and no longer
-  has any effect. Noise gating is now handled automatically by the AIC VAD
-  system. Use `AICFilter.create_vad_analyzer()` for VAD functionality instead.
-
-- Package `pipecat.sync` is deprecated, use `pipecat.utils.sync` instead.
-
-### Fixed
-
-- Fixed bug in `PatternPairAggregator` where pattern handlers could be called
-  multiple times for `KEEP` or `AGGREGATE` patterns.
-
-- Fixed sentence aggregation to correctly handle ambiguous punctuation in
-  streaming text, such as currency ("$29.95") and abbreviations ("Mr. Smith").
-
-- Fixed an issue in `AWSTranscribeSTTService` where the `region` arg was always
-  set to `us-east-1` when providing an AWS_REGION env var.
-
-- Fixed an issue in `SarvamTTSService` where the last sentence was not being
-  spoken. Now, audio is flushed when the TTS services receives the
-  `LLMFullResponseEndFrame` or `EndFrame`.
-
-- Fixed an issue in `DeepgramTTSService` where a `TTSStoppedFrame` was
-  incorrectly pushed after a functional call. This caused an issue with the
-  voice-ui-kit's conversational panel rending of the LLM output after a
-  function call.
-
-- Fixed an issue where `LLMTextFrame.skip_tts` was being overwritten by LLM
-  services.
-
-- Fixed an issue that caused `WebsocketService` instances to attempt
-  reconnection during shutdown.
-
-- Fixed an issue in `ElevenLabsTTSService` where character usage metrics were
-  only reported on the first TTS generation per turn.
-
-## [0.0.96] - 2025-11-26 🦃 "Happy Thanksgiving!" 🦃
-
-### Added
-
-- Added `AWSBedrockAgentCoreProcessor` to support invoking an AgentCore-hosted
-  agent in a Pipecat pipeline.
-
-- Enhanced error handling across the framework:
-
-  - Added `on_error` callback to `FrameProcessor` for centralized error
-    handling.
-
-  - Renamed `push_error(error: ErrorFrame)` to `push_error_frame(error: ErrorFrame)`
-    for clarity.
-
-  - Added new `push_error` method for simplified error reporting:
-
-    ```python
-    async def push_error(error_msg: str,
-                         exception: Optional[Exception] = None,
-                         fatal: bool = False)
-    ```
-
-  - Standardized error logging by replacing `logger.exception` calls with
-    `logger.error` throughout the codebase.
-
-- Added `cache_read_input_tokens`, `cache_creation_input_tokens` and
-  `reasoning_tokens` to OTel spans for LLM call
-
 - Added `LiveKitRESTHelper` utility class for managing LiveKit rooms via REST API.
 
 - Added `DeepgramSageMakerSTTService` which connects to a SageMaker hosted
@@ -223,18 +88,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
   - Added new emotions: calm and fluent
 
-- Added `enable_logging` to `SimliVideoService` input parameters. It's disabled
-  by default.
-
 ### Changed
 
-- Updated `FishAudioTTSService` default model to `s1`.
-
-- Updated `DeepgramTTSService` to use Deepgram's TTS websocket API. ⚠️ This is
-  a potential breaking change, which only affects you if you're self-hosting
-  `DeepgramTTSService`. The new service uses Websockets and improves TTFB
-  latency.
-
 - Updated `daily-python` to 0.22.0.
 
 - `BaseTextAggregator` changes:
@@ -392,11 +247,6 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Fixed
 
-- Fixed an issue in `AWSBedrockLLMService` where the `aws_region` arg was
-  always set to `us-east-1` when providing an AWS_REGION env var.
-
-- Fixed an issue with `DeepgramFluxSTTService` where it sometimes failed to reconnect.
-
 - Fixed an issue in `ElevenLabsRealtimeSTTService` where dynamic language
   updates were not working.
 

COMMUNITY_INTEGRATIONS.md

@@ -79,7 +79,7 @@ Once your PR is submitted, post in the `#community-integrations` Discord channel
 
 **Examples:**
 
-- [NvidiaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/nvidia/stt.py)
+- [RivaSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/riva/stt.py)
 - [FalSTTService](https://github.com/pipecat-ai/pipecat/blob/main/src/pipecat/services/fal/stt.py)
 
 #### Key requirements:

CONTRIBUTING.md

@@ -17,121 +17,24 @@ We welcome contributions of all kinds! Your help is appreciated. Follow these st
    git checkout -b your-branch-name
    ```
 4. **Make your changes**: Edit or add files as necessary.
-5. **Add a changelog entry**: Create a changelog fragment file (see [Changelog Entries](#changelog-entries) below).
-6. **Test your changes**: Ensure that your changes look correct and follow the style set in the codebase.
-7. **Commit your changes**: Once you're satisfied with your changes, commit them with a meaningful message.
+5. **Test your changes**: Ensure that your changes look correct and follow the style set in the codebase.
+6. **Commit your changes**: Once you're satisfied with your changes, commit them with a meaningful message.
 
 ```bash
 git commit -m "Description of your changes"
 ```
 
-8. **Push your changes**: Push your branch to your forked repository.
+7. **Push your changes**: Push your branch to your forked repository.
 
 ```bash
 git push origin your-branch-name
 ```
 
-9. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
+8. **Submit a Pull Request (PR)**: Open a PR from your forked repository to the main branch of this repo.
    > Important: Describe the changes you've made clearly!
 
 Our maintainers will review your PR, and once everything is good, your contributions will be merged!
 
-## Changelog Entries
-
-Every pull request that makes a user-facing change should include a changelog entry. We use a changelog fragment system to avoid merge conflicts.
-
-### Creating a Changelog Fragment
-
-1. Create a new file in the `changelog/` directory with this naming pattern:
-
-   ```
-   <PR_number>.<type>.md
-   ```
-
-2. Choose the appropriate type:
-
-   - `added.md` - New features
-   - `changed.md` - Changes in existing functionality
-   - `deprecated.md` - Soon-to-be removed features
-   - `removed.md` - Removed features
-   - `fixed.md` - Bug fixes
-   - `security.md` - Security fixes
-
-3. Write your changelog entry as a Markdown bullet point. Include the `-` at the start:
-
-**Example files:**
-
-`changelog/1234.added.md`:
-
-```markdown
-- Added support for Anthropic Claude 3.5 Sonnet with improved streaming performance.
-```
-
-`changelog/5678.fixed.md`:
-
-```markdown
-- Fixed an issue where audio frames were dropped during high-load scenarios.
-```
-
-**For entries with nested bullets:**
-
-`changelog/1234.changed.md`:
-
-```markdown
-- Updated service configuration:
-
-  - Changed default timeout to 30 seconds
-  - Added retry logic for failed connections
-```
-
-### Multiple Changes in One PR
-
-**Different types of changes:** Create separate fragment files for each type:
-
-```
-changelog/1234.added.md
-changelog/1234.fixed.md
-```
-
-**Multiple changes of the same type:** Create numbered fragment files:
-
-```
-changelog/1234.changed.md
-changelog/1234.changed.2.md
-```
-
-**Related changes:** Use nested bullets in a single fragment:
-
-```markdown
-- Updated service configuration:
-
-  - Changed default timeout to 30 seconds
-  - Added retry logic for failed connections
-```
-
-**Rule of thumb:** One logical change per fragment file. If changes are unrelated, use separate files.
-
-### Preview Your Changes
-
-To see what your changelog entry will look like:
-
-```bash
-towncrier build --draft --version Unreleased
-```
-
-This won't modify any files, just show you a preview.
-
-### When to Skip Changelog Entries
-
-You can skip adding a changelog entry for:
-
-- Documentation-only changes
-- Internal refactoring with no user-facing impact
-- Test-only changes
-- CI/build configuration changes
-
-If you're unsure whether your change needs a changelog entry, ask in your PR!
-
 ## Dependency Management
 
 This project uses [uv](https://docs.astral.sh/uv/) for dependency management. The `uv.lock` file is committed to ensure reproducible builds.

README.md

@@ -74,9 +74,9 @@ Catch new features, interviews, and how-tos on our [Pipecat TV](https://www.yout
 
 | Category            | Services                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  |
 | ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Gradium](https://docs.pipecat.ai/server/services/stt/gradium), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                          |
+| Speech-to-Text      | [AssemblyAI](https://docs.pipecat.ai/server/services/stt/assemblyai), [AWS](https://docs.pipecat.ai/server/services/stt/aws), [Azure](https://docs.pipecat.ai/server/services/stt/azure), [Cartesia](https://docs.pipecat.ai/server/services/stt/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/stt/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/stt/elevenlabs), [Fal Wizper](https://docs.pipecat.ai/server/services/stt/fal), [Gladia](https://docs.pipecat.ai/server/services/stt/gladia), [Google](https://docs.pipecat.ai/server/services/stt/google), [Groq (Whisper)](https://docs.pipecat.ai/server/services/stt/groq), [NVIDIA Riva](https://docs.pipecat.ai/server/services/stt/riva), [OpenAI (Whisper)](https://docs.pipecat.ai/server/services/stt/openai), [SambaNova (Whisper)](https://docs.pipecat.ai/server/services/stt/sambanova), [Sarvam](https://docs.pipecat.ai/server/services/stt/sarvam), [Soniox](https://docs.pipecat.ai/server/services/stt/soniox), [Speechmatics](https://docs.pipecat.ai/server/services/stt/speechmatics), [Ultravox](https://docs.pipecat.ai/server/services/stt/ultravox), [Whisper](https://docs.pipecat.ai/server/services/stt/whisper)                                                                                                                                                                                          |
 | LLMs                | [Anthropic](https://docs.pipecat.ai/server/services/llm/anthropic), [AWS](https://docs.pipecat.ai/server/services/llm/aws), [Azure](https://docs.pipecat.ai/server/services/llm/azure), [Cerebras](https://docs.pipecat.ai/server/services/llm/cerebras), [DeepSeek](https://docs.pipecat.ai/server/services/llm/deepseek), [Fireworks AI](https://docs.pipecat.ai/server/services/llm/fireworks), [Gemini](https://docs.pipecat.ai/server/services/llm/gemini), [Grok](https://docs.pipecat.ai/server/services/llm/grok), [Groq](https://docs.pipecat.ai/server/services/llm/groq), [Mistral](https://docs.pipecat.ai/server/services/llm/mistral), [NVIDIA NIM](https://docs.pipecat.ai/server/services/llm/nim), [Ollama](https://docs.pipecat.ai/server/services/llm/ollama), [OpenAI](https://docs.pipecat.ai/server/services/llm/openai), [OpenRouter](https://docs.pipecat.ai/server/services/llm/openrouter), [Perplexity](https://docs.pipecat.ai/server/services/llm/perplexity), [Qwen](https://docs.pipecat.ai/server/services/llm/qwen), [SambaNova](https://docs.pipecat.ai/server/services/llm/sambanova) [Together AI](https://docs.pipecat.ai/server/services/llm/together)                                                                                                                                                                                                                              |
-| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Gradium](https://docs.pipecat.ai/server/services/tts/gradium), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
+| Text-to-Speech      | [Async](https://docs.pipecat.ai/server/services/tts/asyncai), [AWS](https://docs.pipecat.ai/server/services/tts/aws), [Azure](https://docs.pipecat.ai/server/services/tts/azure), [Cartesia](https://docs.pipecat.ai/server/services/tts/cartesia), [Deepgram](https://docs.pipecat.ai/server/services/tts/deepgram), [ElevenLabs](https://docs.pipecat.ai/server/services/tts/elevenlabs), [Fish](https://docs.pipecat.ai/server/services/tts/fish), [Google](https://docs.pipecat.ai/server/services/tts/google), [Groq](https://docs.pipecat.ai/server/services/tts/groq), [Hume](https://docs.pipecat.ai/server/services/tts/hume), [Inworld](https://docs.pipecat.ai/server/services/tts/inworld), [LMNT](https://docs.pipecat.ai/server/services/tts/lmnt), [MiniMax](https://docs.pipecat.ai/server/services/tts/minimax), [Neuphonic](https://docs.pipecat.ai/server/services/tts/neuphonic), [NVIDIA Riva](https://docs.pipecat.ai/server/services/tts/riva), [OpenAI](https://docs.pipecat.ai/server/services/tts/openai), [Piper](https://docs.pipecat.ai/server/services/tts/piper), [PlayHT](https://docs.pipecat.ai/server/services/tts/playht), [Rime](https://docs.pipecat.ai/server/services/tts/rime), [Sarvam](https://docs.pipecat.ai/server/services/tts/sarvam), [Speechmatics](https://docs.pipecat.ai/server/services/tts/speechmatics), [XTTS](https://docs.pipecat.ai/server/services/tts/xtts) |
 | Speech-to-Speech    | [AWS Nova Sonic](https://docs.pipecat.ai/server/services/s2s/aws), [Gemini Multimodal Live](https://docs.pipecat.ai/server/services/s2s/gemini), [OpenAI Realtime](https://docs.pipecat.ai/server/services/s2s/openai)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    |
 | Transport           | [Daily (WebRTC)](https://docs.pipecat.ai/server/services/transport/daily), [FastAPI Websocket](https://docs.pipecat.ai/server/services/transport/fastapi-websocket), [SmallWebRTCTransport](https://docs.pipecat.ai/server/services/transport/small-webrtc), [WebSocket Server](https://docs.pipecat.ai/server/services/transport/websocket-server), Local                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                |
 | Serializers         | [Plivo](https://docs.pipecat.ai/server/utilities/serializers/plivo), [Twilio](https://docs.pipecat.ai/server/utilities/serializers/twilio), [Telnyx](https://docs.pipecat.ai/server/utilities/serializers/telnyx)                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |

changelog/_template.md.j2

@@ -1,16 +0,0 @@
-{% for section, _ in sections.items() %}
-{% if sections[section] %}
-{% for category, val in definitions.items() if category in sections[section]%}
-### {{ definitions[category]['name'] }}
-
-{% for text, values in sections[section][category].items() %}
-{{ text }}
-
-{% endfor %}
-{% endfor %}
-{% else %}
-No significant changes.
-
-{% endif %}
-{% endfor %}
-

docs/TURN_AWARE_TRANSCRIPT_PROCESSOR.md

@@ -0,0 +1,103 @@
+# TurnAwareTranscriptProcessor Example
+
+## Overview
+
+The `TurnAwareTranscriptProcessor` combines user and assistant transcript tracking with turn boundary detection. It correctly handles interruptions by only capturing what was actually spoken.
+
+## Basic Usage
+
+```python
+from pipecat.processors.transcript_processor import TurnAwareTranscriptProcessor
+
+# Create the processor
+turn_processor = TurnAwareTranscriptProcessor()
+
+# Register event handlers
+@turn_processor.event_handler("on_turn_started")
+async def handle_turn_started(processor, turn_number):
+    print(f"Turn {turn_number} started")
+
+@turn_processor.event_handler("on_turn_ended")
+async def handle_turn_ended(processor, turn_number, user_text, assistant_text, was_interrupted):
+    print(f"\nTurn {turn_number} ended:")
+    print(f"  User said: {user_text}")
+    print(f"  Assistant said: {assistant_text}")
+    print(f"  Was interrupted: {was_interrupted}")
+
+@turn_processor.event_handler("on_transcript_update")
+async def handle_transcript_update(processor, frame):
+    for msg in frame.messages:
+        print(f"[{msg.role}]: {msg.content}")
+
+# Add to pipeline
+pipeline = Pipeline([
+    transport.input(),
+    stt,
+    turn_processor,  # Process transcripts and track turns
+    context_aggregator.user(),
+    llm,
+    tts,
+    transport.output(),
+    context_aggregator.assistant(),
+])
+```
+
+## Features
+
+1. **Turn Boundary Detection**: Automatically detects when turns start and end based on user and bot speaking patterns
+2. **Interruption Handling**: Correctly captures only what was actually spoken when interruptions occur
+3. **Real-time Transcripts**: Emits transcript messages for both user and assistant speech
+4. **Turn Events**: Provides start/end events with accumulated transcripts for each turn
+
+## Events
+
+### on_turn_started
+Emitted when a new turn begins (user starts speaking).
+
+**Handler signature**: `async def handler(processor, turn_number)`
+
+### on_turn_ended
+Emitted when a turn ends with accumulated transcripts.
+
+**Handler signature**: `async def handler(processor, turn_number, user_transcript, assistant_transcript, was_interrupted)`
+
+### on_transcript_update  
+Inherited from `BaseTranscriptProcessor`, emitted for individual transcript messages.
+
+**Handler signature**: `async def handler(processor, frame)`
+
+## Turn Logic
+
+- Turns start when the user begins speaking (`UserStartedSpeakingFrame`)
+- Turns end when:
+  - The user starts speaking again (previous turn ends, new turn starts)
+  - The bot is interrupted (`InterruptionFrame`)
+  - The pipeline ends (`EndFrame`/`CancelFrame`)
+
+## Integration with OpenTelemetry
+
+You can use turn events to enrich OpenTelemetry spans:
+
+```python
+from pipecat.utils.tracing.turn_trace_observer import TurnTraceObserver
+
+turn_tracker = TurnTrackingObserver()
+turn_tracer = TurnTraceObserver(turn_tracker)
+turn_processor = TurnAwareTranscriptProcessor()
+
+@turn_processor.event_handler("on_turn_ended")
+async def add_transcripts_to_span(processor, turn_number, user_text, assistant_text, interrupted):
+    # Get current span and add transcript data
+    from opentelemetry import trace
+    current_span = trace.get_current_span()
+    if current_span:
+        current_span.set_attribute("turn.user_text", user_text)
+        current_span.set_attribute("turn.assistant_text", assistant_text)
+```
+
+## Notes
+
+- The processor handles async frame processing correctly by delaying turn end until frames are processed
+- Works with word-level timestamps from TTS services like Cartesia
+- Accumulates both user (`TranscriptionFrame`) and assistant (`TTSTextFrame`) speech
+- Emits individual transcript messages in addition to turn-level aggregation

docs/api/conf.py

@@ -119,6 +119,7 @@ def import_core_modules():
         "pipecat.observers",
         "pipecat.runner",
         "pipecat.serializers",
+        "pipecat.sync",
         "pipecat.transcriptions",
         "pipecat.utils",
     ]

docs/api/index.rst

@@ -30,6 +30,7 @@ Quick Links
    Runner <api/pipecat.runner>
    Serializers <api/pipecat.serializers>
    Services <api/pipecat.services>
+   Sync <api/pipecat.sync>
    Transcriptions <api/pipecat.transcriptions>
    Transports <api/pipecat.transports>
-   Utils <api/pipecat.utils>
+   Utils <api/pipecat.utils>

env.example

@@ -73,9 +73,6 @@ GOOGLE_CLOUD_PROJECT_ID=...
 GOOGLE_CLOUD_LOCATION=...
 GOOGLE_TEST_CREDENTIALS=...
 
-# Gradium
-GRAPDIUM_API_KEY=...
-
 # Grok
 GROK_API_KEY=...
 
@@ -194,4 +191,4 @@ TWILIO_AUTH_TOKEN=...
 WHATSAPP_TOKEN=...
 WHATSAPP_WEBHOOK_VERIFICATION_TOKEN=...
 WHATSAPP_PHONE_NUMBER_ID=...
-WHATSAPP_APP_SECRET=...
+WHATSAPP_APP_SECRET=...

examples/foundational/01c-fastpitch.py

@@ -15,7 +15,7 @@ from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineTask
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.nvidia.tts import NvidiaTTSService
+from pipecat.services.riva.tts import FastPitchTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -36,7 +36,7 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    tts = NvidiaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
+    tts = FastPitchTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
 
     task = PipelineTask(
         Pipeline([tts, transport.output()]),

examples/foundational/07af-interruptible-gradium.py

@@ -1,127 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-import os
-
-from dotenv import load_dotenv
-from loguru import logger
-
-from pipecat.audio.turn.smart_turn.base_smart_turn import SmartTurnParams
-from pipecat.audio.turn.smart_turn.local_smart_turn_v3 import LocalSmartTurnAnalyzerV3
-from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.audio.vad.vad_analyzer import VADParams
-from pipecat.frames.frames import LLMRunFrame
-from pipecat.pipeline.pipeline import Pipeline
-from pipecat.pipeline.runner import PipelineRunner
-from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-from pipecat.runner.types import RunnerArguments
-from pipecat.runner.utils import create_transport
-from pipecat.services.gradium.stt import GradiumSTTService
-from pipecat.services.gradium.tts import GradiumTTSService
-from pipecat.services.openai.llm import OpenAILLMService
-from pipecat.transports.base_transport import BaseTransport, TransportParams
-from pipecat.transports.daily.transport import DailyParams
-from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-
-load_dotenv(override=True)
-
-# We store functions so objects (e.g. SileroVADAnalyzer) don't get
-# instantiated. The function will be called when the desired transport gets
-# selected.
-transport_params = {
-    "daily": lambda: DailyParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "twilio": lambda: FastAPIWebsocketParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-    "webrtc": lambda: TransportParams(
-        audio_in_enabled=True,
-        audio_out_enabled=True,
-        vad_analyzer=SileroVADAnalyzer(params=VADParams(stop_secs=0.2)),
-        turn_analyzer=LocalSmartTurnAnalyzerV3(params=SmartTurnParams()),
-    ),
-}
-
-
-async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
-    logger.info(f"Starting bot")
-
-    stt = GradiumSTTService(api_key=os.getenv("GRADIUM_API_KEY"))
-
-    tts = GradiumTTSService(
-        api_key=os.getenv("GRADIUM_API_KEY"),
-        voice_id="YTpq7expH9539ERJ",
-    )
-
-    llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY"))
-
-    messages = [
-        {
-            "role": "system",
-            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be spoken aloud, so avoid special characters that can't easily be spoken, such as emojis or bullet points. Respond to what the user said in a creative and helpful way.",
-        },
-    ]
-
-    context = LLMContext(messages)
-    context_aggregator = LLMContextAggregatorPair(context)
-
-    pipeline = Pipeline(
-        [
-            transport.input(),  # Transport user input
-            stt,
-            context_aggregator.user(),  # User responses
-            llm,  # LLM
-            tts,  # TTS
-            transport.output(),  # Transport bot output
-            context_aggregator.assistant(),  # Assistant spoken responses
-        ]
-    )
-
-    task = PipelineTask(
-        pipeline,
-        params=PipelineParams(
-            enable_metrics=True,
-            enable_usage_metrics=True,
-        ),
-        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
-    )
-
-    @transport.event_handler("on_client_connected")
-    async def on_client_connected(transport, client):
-        logger.info(f"Client connected")
-        # Kick off the conversation.
-        messages.append({"role": "system", "content": "Please introduce yourself to the user."})
-        await task.queue_frames([LLMRunFrame()])
-
-    @transport.event_handler("on_client_disconnected")
-    async def on_client_disconnected(transport, client):
-        logger.info(f"Client disconnected")
-        await task.cancel()
-
-    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
-
-    await runner.run(task)
-
-
-async def bot(runner_args: RunnerArguments):
-    """Main bot entry point compatible with Pipecat Cloud."""
-    transport = await create_transport(runner_args, transport_params)
-    await run_bot(transport, runner_args)
-
-
-if __name__ == "__main__":
-    from pipecat.runner.run import main
-
-    main()

examples/foundational/07r-interruptible-riva-nim.py

@@ -22,9 +22,9 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
-from pipecat.services.nvidia.llm import NvidiaLLMService
-from pipecat.services.nvidia.stt import NvidiaSTTService
-from pipecat.services.nvidia.tts import NvidiaTTSService
+from pipecat.services.nim.llm import NimLLMService
+from pipecat.services.riva.stt import RivaSTTService
+from pipecat.services.riva.tts import RivaTTSService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -59,13 +59,11 @@ transport_params = {
 async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     logger.info(f"Starting bot")
 
-    stt = NvidiaSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
+    stt = RivaSTTService(api_key=os.getenv("NVIDIA_API_KEY"))
 
-    llm = NvidiaLLMService(
-        api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct"
-    )
+    llm = NimLLMService(api_key=os.getenv("NVIDIA_API_KEY"), model="meta/llama-3.1-405b-instruct")
 
-    tts = NvidiaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
+    tts = RivaTTSService(api_key=os.getenv("NVIDIA_API_KEY"))
 
     messages = [
         {

examples/foundational/14i-function-calling-fireworks.py

@@ -76,7 +76,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
 
     llm = FireworksLLMService(
         api_key=os.getenv("FIREWORKS_API_KEY"),
-        model="accounts/fireworks/models/gpt-oss-20b",
+        model="accounts/fireworks/models/llama-v3p1-405b-instruct",
     )
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.

examples/foundational/14j-function-calling-nim.py

@@ -27,7 +27,7 @@ from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams
-from pipecat.services.nvidia.llm import NvidiaLLMService
+from pipecat.services.nim.llm import NimLLMService
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
@@ -75,11 +75,11 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         # text_filters=[MarkdownTextFilter()],
     )
 
-    llm = NvidiaLLMService(
+    llm = NimLLMService(
         api_key=os.getenv("NVIDIA_API_KEY"),
         model="nvidia/llama-3.3-nemotron-super-49b-v1.5",
         # Recommended when turning thinking off
-        params=NvidiaLLMService.InputParams(temperature=0.0),
+        params=NimLLMService.InputParams(temperature=0.0),
     )
     # You can also register a function_name of None to get all functions
     # sent to the same callback with an additional function_name parameter.

examples/foundational/19-openai-realtime.py

@@ -14,13 +14,20 @@ from loguru import logger
 
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
 from pipecat.audio.vad.silero import SileroVADAnalyzer
-from pipecat.frames.frames import LLMRunFrame, LLMSetToolsFrame, TranscriptionMessage
+from pipecat.frames.frames import (
+    LLMRunFrame,
+    LLMSetToolsFrame,
+    LLMUpdateSettingsFrame,
+    TranscriptionMessage,
+)
 from pipecat.observers.loggers.transcription_log_observer import TranscriptionLogObserver
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.transcript_processor import TranscriptProcessor
 from pipecat.runner.types import RunnerArguments

examples/foundational/19a-azure-realtime.py

@@ -19,6 +19,7 @@ from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
 from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response import LLMAssistantAggregatorParams
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport

examples/foundational/22-natural-conversation.py

@@ -28,10 +28,10 @@ from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import LLMService
 from pipecat.services.openai.llm import OpenAIContextAggregatorPair, OpenAILLMService
+from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.sync.event_notifier import EventNotifier
 
 load_dotenv(override=True)
 

examples/foundational/22b-natural-conversation-proposal.py

@@ -45,11 +45,11 @@ from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams, LLMService
 from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.sync.base_notifier import BaseNotifier
+from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.sync.base_notifier import BaseNotifier
-from pipecat.utils.sync.event_notifier import EventNotifier
 from pipecat.utils.time import time_now_iso8601
 
 load_dotenv(override=True)

examples/foundational/22c-natural-conversation-mixed-llms.py

@@ -46,11 +46,11 @@ from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.deepgram.stt import DeepgramSTTService
 from pipecat.services.llm_service import FunctionCallParams, LLMService
 from pipecat.services.openai.llm import OpenAILLMService
+from pipecat.sync.base_notifier import BaseNotifier
+from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.sync.base_notifier import BaseNotifier
-from pipecat.utils.sync.event_notifier import EventNotifier
 from pipecat.utils.time import time_now_iso8601
 
 load_dotenv(override=True)

examples/foundational/22d-natural-conversation-gemini-audio.py

@@ -47,11 +47,11 @@ from pipecat.runner.utils import create_transport
 from pipecat.services.cartesia.tts import CartesiaTTSService
 from pipecat.services.google.llm import GoogleLLMService
 from pipecat.services.llm_service import LLMService
+from pipecat.sync.base_notifier import BaseNotifier
+from pipecat.sync.event_notifier import EventNotifier
 from pipecat.transports.base_transport import BaseTransport, TransportParams
 from pipecat.transports.daily.transport import DailyParams
 from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
-from pipecat.utils.sync.base_notifier import BaseNotifier
-from pipecat.utils.sync.event_notifier import EventNotifier
 from pipecat.utils.time import time_now_iso8601
 
 load_dotenv(override=True)

examples/foundational/39-mcp-stdio.py

@@ -64,14 +64,11 @@ class UrlToImageProcessor(FrameProcessor):
             await self.push_frame(frame, direction)
 
     def extract_url(self, text: str):
-        try:
-            data = json.loads(text)
-            if "artObject" in data:
-                return data["artObject"]["webImage"]["url"]
-            if "artworks" in data and len(data["artworks"]):
-                return data["artworks"][0]["webImage"]["url"]
-        except:
-            pass
+        data = json.loads(text)
+        if "artObject" in data:
+            return data["artObject"]["webImage"]["url"]
+        if "artworks" in data and len(data["artworks"]):
+            return data["artworks"][0]["webImage"]["url"]
 
         return None
 
@@ -91,23 +88,6 @@ class UrlToImageProcessor(FrameProcessor):
             logger.error(error_msg)
 
 
-# full list of tools available from rijksmuseum MCP:
-# - get_artwork_details
-# - get_artwork_image
-# - get_user_sets
-# - get_user_set_details
-# - open_image_in_browser
-# - get_artist_timeline
-
-mcp_tools_filter = ["get_artwork_details", "get_artwork_image", "open_image_in_browser"]
-
-
-def open_image_output_filter(output: str):
-    pattern = r"Successfully opened image in browser: "
-    text_to_print = re.sub(pattern, "", output)
-    print(f"🖼️ link to high resolution artwork: {text_to_print}")
-
-
 # We store functions so objects (e.g. SileroVADAnalyzer) don't get
 # instantiated. The function will be called when the desired transport gets
 # selected.
@@ -156,10 +136,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
                     # https://github.com/r-huijts/rijksmuseum-mcp
                     args=["-y", "mcp-server-rijksmuseum"],
                     env={"RIJKSMUSEUM_API_KEY": os.getenv("RIJKSMUSEUM_API_KEY")},
-                ),
-                # Optional
-                tools_filter=mcp_tools_filter,  # Optional
-                tools_output_filters={"open_image_in_browser": open_image_output_filter},
+                )
             )
         except Exception as e:
             logger.error(f"error setting up mcp")

examples/foundational/39c-multiple-mcp.py

@@ -67,14 +67,13 @@ class UrlToImageProcessor(FrameProcessor):
             await self.push_frame(frame, direction)
 
     def extract_url(self, text: str):
-        try:
-            data = json.loads(text)
-            if "artObject" in data:
-                return data["artObject"]["webImage"]["url"]
-            if "artworks" in data and len(data["artworks"]):
-                return data["artworks"][0]["webImage"]["url"]
-        except:
-            pass
+        data = json.loads(text)
+        if "artObject" in data:
+            return data["artObject"]["webImage"]["url"]
+        if "artworks" in data and len(data["artworks"]):
+            return data["artworks"][0]["webImage"]["url"]
+
+        return None
 
     async def run_image_process(self, image_url: str):
         try:

pyproject.toml

@@ -45,7 +45,7 @@ Source = "https://github.com/pipecat-ai/pipecat"
 Website = "https://pipecat.ai"
 
 [project.optional-dependencies]
-aic = [ "aic-sdk~=1.2.0" ]
+aic = [ "aic-sdk~=1.1.0" ]
 anthropic = [ "anthropic~=0.49.0" ]
 assemblyai = [ "pipecat-ai[websockets-base]" ]
 asyncai = [ "pipecat-ai[websockets-base]" ]
@@ -55,7 +55,7 @@ azure = [ "azure-cognitiveservices-speech~=1.42.0"]
 cartesia = [ "cartesia~=2.0.3", "pipecat-ai[websockets-base]" ]
 cerebras = []
 daily = [ "daily-python~=0.22.0" ]
-deepgram = [ "deepgram-sdk~=4.7.0", "pipecat-ai[websockets-base]" ]
+deepgram = [ "deepgram-sdk~=4.7.0" ]
 deepseek = []
 elevenlabs = [ "pipecat-ai[websockets-base]" ]
 fal = [ "fal-client~=0.5.9" ]
@@ -63,7 +63,6 @@ fireworks = []
 fish = [ "ormsgpack~=1.7.0", "pipecat-ai[websockets-base]" ]
 gladia = [ "pipecat-ai[websockets-base]" ]
 google = [ "google-cloud-speech>=2.33.0,<3", "google-cloud-texttospeech>=2.31.0,<3", "google-genai>=1.41.0,<2", "pipecat-ai[websockets-base]" ]
-gradium = [ "pipecat-ai[websockets-base]" ]
 grok = []
 groq = [ "groq~=0.23.0" ]
 gstreamer = [ "pygobject~=3.50.0" ]
@@ -84,8 +83,8 @@ mistral = []
 mlx-whisper = [ "mlx-whisper~=0.4.2" ]
 moondream = [ "accelerate~=1.10.0", "einops~=0.8.0", "pyvips[binary]~=3.0.0", "timm~=1.0.13", "transformers>=4.48.0" ]
 neuphonic = [ "pipecat-ai[websockets-base]" ]
+nim = []
 noisereduce = [ "noisereduce~=3.0.3" ]
-nvidia = [ "nvidia-riva-client~=2.21.1" ]
 openai = [ "pipecat-ai[websockets-base]" ]
 openpipe = [ "openpipe>=4.50.0,<6" ]
 openrouter = []
@@ -94,7 +93,7 @@ playht = [ "pipecat-ai[websockets-base]" ]
 qwen = []
 remote-smart-turn = []
 rime = [ "pipecat-ai[websockets-base]" ]
-riva = [ "pipecat-ai[nvidia]" ]
+riva = [ "nvidia-riva-client~=2.21.1" ]
 runner = [ "python-dotenv>=1.0.0,<2.0.0", "uvicorn>=0.32.0,<1.0.0", "fastapi>=0.115.6,<0.122.0", "pipecat-ai-small-webrtc-prebuilt>=1.0.0"]
 sagemaker = ["aws_sdk_sagemaker_runtime_http2; python_version>='3.12'"]
 sambanova = []
@@ -130,7 +129,6 @@ dev = [
     "setuptools~=78.1.1",
     "setuptools_scm~=8.3.1",
     "python-dotenv>=1.0.1,<2.0.0",
-    "towncrier~=25.8.0",
 ]
 
 docs = [
@@ -161,7 +159,7 @@ where = ["src"]
     "src/pipecat/audio/dtmf/dtmf-star.wav",
 ]
 "pipecat.services.aws_nova_sonic" = ["src/pipecat/services/aws_nova_sonic/ready.wav"]
-"pipecat.audio.turn.smart_turn.data" = ["src/pipecat/audio/turn/smart_turn/data/smart-turn-v3.1-cpu.onnx"]
+"pipecat.audio.turn.smart_turn.data" = ["src/pipecat/audio/turn/smart_turn/data/smart-turn-v3.0.onnx"]
 
 [tool.pytest.ini_options]
 addopts = "--verbose"
@@ -208,44 +206,3 @@ convention = "google"
 command_line = "--module pytest"
 source = ["src"]
 omit = ["*/tests/*"]
-
-[tool.towncrier]
-package = "pipecat"
-package_dir = "src"
-filename = "CHANGELOG.md"
-directory = "changelog"
-start_string = "<!-- towncrier release notes start -->\n"
-template = "changelog/_template.md.j2"
-title_format = "## [{version}] - {project_date}"
-underlines = ["", "", ""]
-wrap = true
-
-[[tool.towncrier.type]]
-directory = "added"
-name = "Added"
-showcontent = true
-
-[[tool.towncrier.type]]
-directory = "changed"
-name = "Changed"
-showcontent = true
-
-[[tool.towncrier.type]]
-directory = "deprecated"
-name = "Deprecated"
-showcontent = true
-
-[[tool.towncrier.type]]
-directory = "removed"
-name = "Removed"
-showcontent = true
-
-[[tool.towncrier.type]]
-directory = "fixed"
-name = "Fixed"
-showcontent = true
-
-[[tool.towncrier.type]]
-directory = "security"
-name = "Security"
-showcontent = true

scripts/evals/run-release-evals.py

@@ -103,7 +103,7 @@ TESTS_07 = [
     ("07o-interruptible-assemblyai.py", EVAL_SIMPLE_MATH),
     ("07q-interruptible-rime.py", EVAL_SIMPLE_MATH),
     ("07q-interruptible-rime-http.py", EVAL_SIMPLE_MATH),
-    ("07r-interruptible-nvidia.py", EVAL_SIMPLE_MATH),
+    ("07r-interruptible-riva-nim.py", EVAL_SIMPLE_MATH),
     ("07s-interruptible-google-audio-in.py", EVAL_SIMPLE_MATH),
     ("07t-interruptible-fish.py", EVAL_SIMPLE_MATH),
     ("07v-interruptible-neuphonic.py", EVAL_SIMPLE_MATH),
@@ -136,7 +136,7 @@ TESTS_14 = [
     ("14g-function-calling-grok.py", EVAL_WEATHER),
     ("14h-function-calling-azure.py", EVAL_WEATHER),
     ("14i-function-calling-fireworks.py", EVAL_WEATHER),
-    ("14j-function-calling-nvidia.py", EVAL_WEATHER),
+    ("14j-function-calling-nim.py", EVAL_WEATHER),
     ("14k-function-calling-cerebras.py", EVAL_WEATHER),
     ("14m-function-calling-openrouter.py", EVAL_WEATHER),
     ("14n-function-calling-perplexity.py", EVAL_WEATHER),

src/pipecat/audio/filters/aic_filter.py

@@ -39,7 +39,7 @@ class AICFilter(BaseAudioFilter):
         self,
         *,
         license_key: str = "",
-        model_type: AICModelType = AICModelType.QUAIL_STT,
+        model_type: AICModelType = AICModelType.QUAIL_L,
         enhancement_level: Optional[float] = 1.0,
         voice_gain: Optional[float] = 1.0,
         noise_gate_enable: Optional[bool] = True,
@@ -52,27 +52,12 @@ class AICFilter(BaseAudioFilter):
             enhancement_level: Optional overall enhancement strength (0.0..1.0).
             voice_gain: Optional linear gain applied to detected speech (0.0..4.0).
             noise_gate_enable: Optional enable/disable noise gate (default: True).
-
-                .. deprecated:: 1.3.0
-                    The `noise_gate_enable` parameter is deprecated and no longer has any effect.
-                    It will be removed in a future version.
         """
         self._license_key = license_key
         self._model_type = model_type
 
         self._enhancement_level = enhancement_level
         self._voice_gain = voice_gain
-        if noise_gate_enable is not None:
-            import warnings
-
-            with warnings.catch_warnings():
-                warnings.simplefilter("always")
-                warnings.warn(
-                    "Parameter `noise_gate_enable` is deprecated and no longer has any effect. "
-                    "It will be removed in a future version. Use AIC VAD instead (create_vad_analyzer()).",
-                    DeprecationWarning,
-                )
-
         self._noise_gate_enable = noise_gate_enable
 
         self._enabled = True
@@ -164,6 +149,10 @@ class AICFilter(BaseAudioFilter):
                 )
             if self._voice_gain is not None:
                 self._aic.set_parameter(AICParameter.VOICE_GAIN, float(self._voice_gain))
+            if self._noise_gate_enable is not None:
+                self._aic.set_parameter(
+                    AICParameter.NOISE_GATE_ENABLE, 1.0 if bool(self._noise_gate_enable) else 0.0
+                )
 
             self._aic_ready = True
 

src/pipecat/audio/turn/smart_turn/base_smart_turn.py

@@ -28,6 +28,7 @@ from pipecat.metrics.metrics import MetricsData, SmartTurnMetricsData
 STOP_SECS = 3
 PRE_SPEECH_MS = 0
 MAX_DURATION_SECONDS = 8  # Max allowed segment duration
+USE_ONLY_LAST_VAD_SEGMENT = True
 
 
 class SmartTurnParams(BaseTurnParams):
@@ -42,6 +43,8 @@ class SmartTurnParams(BaseTurnParams):
     stop_secs: float = STOP_SECS
     pre_speech_ms: float = PRE_SPEECH_MS
     max_duration_secs: float = MAX_DURATION_SECONDS
+    # not exposing this for now yet until the model can handle it.
+    # use_only_last_vad_segment: bool = USE_ONLY_LAST_VAD_SEGMENT
 
 
 class SmartTurnTimeoutException(Exception):
@@ -157,7 +160,7 @@ class BaseSmartTurn(BaseTurnAnalyzer):
         state, result = await loop.run_in_executor(
             self._executor, self._process_speech_segment, self._audio_buffer
         )
-        if state == EndOfTurnState.COMPLETE:
+        if state == EndOfTurnState.COMPLETE or USE_ONLY_LAST_VAD_SEGMENT:
             self._clear(state)
         logger.debug(f"End of Turn result: {state}")
         return state, result

src/pipecat/audio/turn/smart_turn/local_smart_turn_v3.py

@@ -42,15 +42,17 @@ class LocalSmartTurnAnalyzerV3(BaseSmartTurn):
 
         Args:
             smart_turn_model_path: Path to the ONNX model file. If this is not
-                set, the bundled smart-turn-v3.1-cpu model will be used.
+                set, the bundled smart-turn-v3.0 model will be used.
             cpu_count: The number of CPUs to use for inference. Defaults to 1.
             **kwargs: Additional arguments passed to BaseSmartTurn.
         """
         super().__init__(**kwargs)
 
+        logger.debug("Loading Local Smart Turn v3 model...")
+
         if not smart_turn_model_path:
             # Load bundled model
-            model_name = "smart-turn-v3.1-cpu.onnx"
+            model_name = "smart-turn-v3.0.onnx"
             package_path = "pipecat.audio.turn.smart_turn.data"
 
             try:
@@ -68,8 +70,6 @@ class LocalSmartTurnAnalyzerV3(BaseSmartTurn):
                         impresources.files(package_path).joinpath(model_name)
                     )
 
-        logger.debug(f"Loading Local Smart Turn v3.x model from {smart_turn_model_path}...")
-
         so = ort.SessionOptions()
         so.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
         so.inter_op_num_threads = 1
@@ -79,7 +79,7 @@ class LocalSmartTurnAnalyzerV3(BaseSmartTurn):
         self._feature_extractor = WhisperFeatureExtractor(chunk_length=8)
         self._session = ort.InferenceSession(smart_turn_model_path, sess_options=so)
 
-        logger.debug("Loaded Local Smart Turn v3.x")
+        logger.debug("Loaded Local Smart Turn v3")
 
     def _predict_endpoint(self, audio_array: np.ndarray) -> Dict[str, Any]:
         """Predict end-of-turn using local ONNX model."""

src/pipecat/extensions/ivr/ivr_navigator.py

@@ -18,10 +18,8 @@ from loguru import logger
 from pipecat.audio.dtmf.types import KeypadEntry
 from pipecat.audio.vad.vad_analyzer import VADParams
 from pipecat.frames.frames import (
-    EndFrame,
     Frame,
     LLMContextFrame,
-    LLMFullResponseEndFrame,
     LLMMessagesUpdateFrame,
     LLMTextFrame,
     OutputDTMFUrgentFrame,
@@ -151,18 +149,11 @@ class IVRProcessor(FrameProcessor):
 
         elif isinstance(frame, LLMTextFrame):
             # Process text through the pattern aggregator
-            async for result in self._aggregator.aggregate(frame.text):
+            result = await self._aggregator.aggregate(frame.text)
+            if result:
                 # Push aggregated text that doesn't contain XML patterns
                 await self.push_frame(LLMTextFrame(result.text), direction)
 
-        elif isinstance(frame, (LLMFullResponseEndFrame, EndFrame)):
-            # Flush any remaining text from the aggregator
-            remaining = await self._aggregator.flush()
-            if remaining:
-                await self.push_frame(LLMTextFrame(remaining.text), direction)
-            # Push the end frame
-            await self.push_frame(frame, direction)
-
         else:
             await self.push_frame(frame, direction)
 

src/pipecat/extensions/voicemail/voicemail_detector.py

@@ -40,8 +40,8 @@ from pipecat.processors.aggregators.llm_context import LLMContext
 from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor, FrameProcessorSetup
 from pipecat.services.llm_service import LLMService
-from pipecat.utils.sync.base_notifier import BaseNotifier
-from pipecat.utils.sync.event_notifier import EventNotifier
+from pipecat.sync.base_notifier import BaseNotifier
+from pipecat.sync.event_notifier import EventNotifier
 
 
 class NotifierGate(FrameProcessor):

src/pipecat/frames/frames.py

@@ -330,7 +330,7 @@ class TextFrame(DataFrame):
     """
 
     text: str
-    skip_tts: Optional[bool] = field(init=False)
+    skip_tts: bool = field(init=False)
     # Whether any necessary inter-frame (leading/trailing) spaces are already
     # included in the text.
     # NOTE: Ideally this would be available at init time with a default value,
@@ -343,7 +343,7 @@ class TextFrame(DataFrame):
 
     def __post_init__(self):
         super().__post_init__()
-        self.skip_tts = None
+        self.skip_tts = False
         self.includes_inter_frame_spaces = False
         self.append_to_context = True
 
@@ -835,13 +835,11 @@ class ErrorFrame(SystemFrame):
         error: Description of the error that occurred.
         fatal: Whether the error is fatal and requires bot shutdown.
         processor: The frame processor that generated the error.
-        exception: The exception that occurred.
     """
 
     error: str
     fatal: bool = False
     processor: Optional["FrameProcessor"] = None
-    exception: Optional[Exception] = None
 
     def __str__(self):
         return f"{self.name}(error: {self.error}, fatal: {self.fatal})"
@@ -1632,22 +1630,22 @@ class LLMFullResponseStartFrame(ControlFrame):
     more TextFrames and a final LLMFullResponseEndFrame.
     """
 
-    skip_tts: Optional[bool] = field(init=False)
+    skip_tts: bool = field(init=False)
 
     def __post_init__(self):
         super().__post_init__()
-        self.skip_tts = None
+        self.skip_tts = False
 
 
 @dataclass
 class LLMFullResponseEndFrame(ControlFrame):
     """Frame indicating the end of an LLM response."""
 
-    skip_tts: Optional[bool] = field(init=False)
+    skip_tts: bool = field(init=False)
 
     def __post_init__(self):
         super().__post_init__()
-        self.skip_tts = None
+        self.skip_tts = False
 
 
 @dataclass

src/pipecat/processors/aggregators/gated_llm_context.py

@@ -9,7 +9,7 @@
 from pipecat.frames.frames import CancelFrame, EndFrame, Frame, LLMContextFrame, StartFrame
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.utils.sync.base_notifier import BaseNotifier
+from pipecat.sync.base_notifier import BaseNotifier
 
 
 class GatedLLMContextAggregator(FrameProcessor):

src/pipecat/processors/aggregators/llm_text_processor.py

@@ -83,7 +83,8 @@ class LLMTextProcessor(FrameProcessor):
         await self._text_aggregator.reset()
 
     async def _handle_llm_text(self, in_frame: LLMTextFrame):
-        async for aggregation in self._text_aggregator.aggregate(in_frame.text):
+        aggregation = await self._text_aggregator.aggregate(in_frame.text)
+        if aggregation:
             out_frame = AggregatedTextFrame(
                 text=aggregation.text,
                 aggregated_by=aggregation.type,
@@ -91,13 +92,15 @@ class LLMTextProcessor(FrameProcessor):
             out_frame.skip_tts = in_frame.skip_tts
             await self.push_frame(out_frame)
 
-    async def _handle_llm_end(self, skip_tts: Optional[bool] = None):
-        # Flush any remaining text
-        remaining = await self._text_aggregator.flush()
-        if remaining:
+    async def _handle_llm_end(self, skip_tts: bool = False):
+        # Flush any remaining aggregated text at the end of the LLM response
+        aggregation = self._text_aggregator.text
+        await self._text_aggregator.reset()
+        text = aggregation.text.strip()
+        if text:
             out_frame = AggregatedTextFrame(
-                text=remaining.text,
-                aggregated_by=remaining.type,
+                text=text,
+                aggregated_by=aggregation.type,
             )
             out_frame.skip_tts = skip_tts
             await self.push_frame(out_frame)

src/pipecat/processors/filters/wake_check_filter.py

@@ -126,4 +126,6 @@ class WakeCheckFilter(FrameProcessor):
             else:
                 await self.push_frame(frame, direction)
         except Exception as e:
-            await self.push_error(error_msg=f"Error in wake word filter: {e}", exception=e)
+            error_msg = f"Error in wake word filter: {e}"
+            logger.exception(error_msg)
+            await self.push_error(ErrorFrame(error_msg))

src/pipecat/processors/filters/wake_notifier_filter.py

@@ -10,7 +10,7 @@ from typing import Awaitable, Callable, Tuple, Type
 
 from pipecat.frames.frames import Frame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
-from pipecat.utils.sync.base_notifier import BaseNotifier
+from pipecat.sync.base_notifier import BaseNotifier
 
 
 class WakeNotifierFilter(FrameProcessor):

src/pipecat/processors/frame_processor.py

@@ -12,7 +12,6 @@ management, and frame flow control mechanisms.
 """
 
 import asyncio
-import traceback
 from dataclasses import dataclass
 from enum import Enum
 from typing import Any, Awaitable, Callable, Coroutine, List, Optional, Sequence, Tuple, Type
@@ -143,7 +142,6 @@ class FrameProcessor(BaseObject):
     - on_after_process_frame: Called after a frame is processed
     - on_before_push_frame: Called before a frame is pushed
     - on_after_push_frame: Called after a frame is pushed
-    - on_error: Called when an error is raised in the frame processing.
     """
 
     def __init__(
@@ -236,7 +234,6 @@ class FrameProcessor(BaseObject):
         self._register_event_handler("on_after_process_frame", sync=True)
         self._register_event_handler("on_before_push_frame", sync=True)
         self._register_event_handler("on_after_push_frame", sync=True)
-        self._register_event_handler("on_error", sync=True)
 
     @property
     def id(self) -> int:
@@ -633,43 +630,7 @@ class FrameProcessor(BaseObject):
         elif isinstance(frame, (FrameProcessorResumeFrame, FrameProcessorResumeUrgentFrame)):
             await self.__resume(frame)
 
-    async def push_error(
-        self,
-        error_msg: str,
-        exception: Optional[Exception] = None,
-        fatal: bool = False,
-    ):
-        """Creates and pushes an ErrorFrame upstream.
-
-        Creates and pushes an ErrorFrame upstream to notify other processors in the
-        pipeline about an error condition. The error frame will include context about
-        which processor generated the error.
-
-        Args:
-            error_msg: Descriptive message explaining the error condition.
-            exception: Optional exception object that caused the error, if available.
-                This provides additional context for debugging and error handling.
-            fatal: Whether this error should be considered fatal to the pipeline.
-                Fatal errors typically cause the entire pipeline to stop processing.
-                Defaults to False for non-fatal errors.
-
-        Example::
-
-            ```python
-            # Non-fatal error
-            await self.push_error("Failed to process audio chunk, skipping")
-
-            # Fatal error with exception context
-            try:
-                result = some_critical_operation()
-            except Exception as e:
-                await self.push_error("Critical operation failed", exception=e, fatal=True)
-            ```
-        """
-        error_frame = ErrorFrame(error=error_msg, fatal=fatal, exception=exception, processor=self)
-        await self.push_error_frame(error=error_frame)
-
-    async def push_error_frame(self, error: ErrorFrame):
+    async def push_error(self, error: ErrorFrame):
         """Push an error frame upstream.
 
         Args:
@@ -677,18 +638,6 @@ class FrameProcessor(BaseObject):
         """
         if not error.processor:
             error.processor = self
-        await self._call_event_handler("on_error", error)
-
-        if error.exception:
-            tb = traceback.extract_tb(error.exception.__traceback__)
-            last = tb[-1]
-            error_message = (
-                f"{error.processor} exception ({last.filename}:{last.lineno}): {error.error}"
-            )
-        else:
-            error_message = f"{error.processor} error: {error.error}"
-
-        logger.error(error_message)
         await self.push_frame(error, FrameDirection.UPSTREAM)
 
     async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
@@ -810,10 +759,8 @@ class FrameProcessor(BaseObject):
                 await self.__cancel_process_task()
                 self.__create_process_task()
         except Exception as e:
-            await self.push_error(
-                error_msg=f"Uncaught exception handling _start_interruption: {e}",
-                exception=e,
-            )
+            logger.exception(f"Uncaught exception in {self} when handling _start_interruption: {e}")
+            await self.push_error(ErrorFrame(str(e)))
 
     async def __internal_push_frame(self, frame: Frame, direction: FrameDirection):
         """Internal method to push frames to adjacent processors.
@@ -850,7 +797,8 @@ class FrameProcessor(BaseObject):
                     await self._observer.on_push_frame(data)
                 await self._prev.queue_frame(frame, direction)
         except Exception as e:
-            await self.push_error(error_msg=f"Uncaught exception: {e}", exception=e)
+            logger.exception(f"Uncaught exception in {self}: {e}")
+            await self.push_error(ErrorFrame(str(e)))
 
     def _check_started(self, frame: Frame):
         """Check if the processor has been started.
@@ -926,7 +874,8 @@ class FrameProcessor(BaseObject):
 
             await self._call_event_handler("on_after_process_frame", frame)
         except Exception as e:
-            await self.push_error(error_msg=f"Error processing frame: {e}", exception=e)
+            logger.exception(f"{self}: error processing frame: {e}")
+            await self.push_error(ErrorFrame(str(e)))
 
     async def __input_frame_task_handler(self):
         """Handle frames from the input queue.

src/pipecat/processors/frameworks/langchain.py

@@ -24,7 +24,7 @@ try:
     from langchain_core.messages import AIMessageChunk
     from langchain_core.runnables import Runnable
 except ModuleNotFoundError as e:
-    logger.error("In order to use Langchain, you need to `pip install pipecat-ai[langchain]`. ")
+    logger.exception("In order to use Langchain, you need to `pip install pipecat-ai[langchain]`. ")
     raise Exception(f"Missing module: {e}")
 
 
@@ -113,6 +113,6 @@ class LangchainProcessor(FrameProcessor):
         except GeneratorExit:
             logger.warning(f"{self} generator was closed prematurely")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} an unknown error occurred: {e}")
         finally:
             await self.push_frame(LLMFullResponseEndFrame())

src/pipecat/processors/frameworks/rtvi.py

@@ -935,8 +935,8 @@ class RTVIObserverParams:
         system_logs_enabled: Indicates if system logs should be sent.
         errors_enabled: [Deprecated] Indicates if errors messages should be sent.
         skip_aggregator_types: List of aggregation types to skip sending as tts/output messages.
-            Note: if using this to avoid sending secure information, be sure to also disable
-            bot_llm_enabled to avoid leaking through LLM messages.
+          Note: if using this to avoid sending secure information, be sure to also disable
+                bot_llm_enabled to avoid leaking through LLM messages.
         bot_output_transforms: A list of callables to transform text before just before sending it
             to TTS. Each callable takes the aggregated text and its type, and returns the
             transformed text. To register, provide a list of tuples of

src/pipecat/processors/frameworks/strands_agents.py

@@ -23,7 +23,7 @@ try:
     from strands import Agent
     from strands.multiagent.graph import Graph
 except ModuleNotFoundError as e:
-    logger.error("In order to use Strands Agents, you need to `pip install strands-agents`.")
+    logger.exception("In order to use Strands Agents, you need to `pip install strands-agents`.")
     raise Exception(f"Missing module: {e}")
 
 
@@ -143,7 +143,7 @@ class StrandsAgentsProcessor(FrameProcessor):
         except GeneratorExit:
             logger.warning(f"{self} generator was closed prematurely")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} an unknown error occurred: {e}")
         finally:
             if ttfb_tracking:
                 await self.stop_ttfb_metrics()

src/pipecat/processors/transcript_processor.py

@@ -15,6 +15,7 @@ from typing import List, Optional
 from loguru import logger
 
 from pipecat.frames.frames import (
+    BotStartedSpeakingFrame,
     BotStoppedSpeakingFrame,
     CancelFrame,
     EndFrame,
@@ -24,6 +25,7 @@ from pipecat.frames.frames import (
     TranscriptionMessage,
     TranscriptionUpdateFrame,
     TTSTextFrame,
+    UserStartedSpeakingFrame,
 )
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.utils.string import TextPartForConcatenation, concatenate_aggregated_text
@@ -306,3 +308,267 @@ class TranscriptProcessor:
             return handler
 
         return decorator
+
+
+class TurnAwareTranscriptProcessor(BaseTranscriptProcessor):
+    """Processes transcripts with turn boundary awareness.
+
+    This processor combines user and assistant transcript tracking with turn
+    detection, emitting events when turns start and end. It correctly handles
+    interruptions by only capturing what was actually spoken.
+
+    Turn boundaries are detected based on:
+    - User started speaking (UserStartedSpeakingFrame)
+    - Bot stopped speaking (BotStoppedSpeakingFrame)
+    - Interruptions (InterruptionFrame)
+
+    Events:
+        on_turn_started: Emitted when a new turn begins.
+            Handler signature: async def handler(processor, turn_number)
+
+        on_turn_ended: Emitted when a turn ends.
+            Handler signature: async def handler(processor, turn_number,
+                                                user_transcript, assistant_transcript,
+                                                was_interrupted)
+
+        on_transcript_update: Inherited from BaseTranscriptProcessor, emitted for
+            individual transcript messages.
+
+    Example::
+
+        turn_processor = TurnAwareTranscriptProcessor()
+
+        @turn_processor.event_handler("on_turn_started")
+        async def handle_turn_started(processor, turn_number):
+            print(f"Turn {turn_number} started")
+
+        @turn_processor.event_handler("on_turn_ended")
+        async def handle_turn_ended(processor, turn_number, user_text, assistant_text, interrupted):
+            print(f"Turn {turn_number} ended")
+            print(f"User said: {user_text}")
+            print(f"Assistant said: {assistant_text}")
+            print(f"Was interrupted: {interrupted}")
+
+        pipeline = Pipeline([
+            transport.input(),
+            stt,
+            turn_processor,
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ])
+    """
+
+    def __init__(self, **kwargs):
+        """Initialize the turn-aware transcript processor.
+
+        Args:
+            **kwargs: Additional arguments passed to parent class.
+        """
+        super().__init__(**kwargs)
+
+        # Turn tracking state
+        self._turn_number = 0
+        self._turn_active = False
+        self._turn_start_time: Optional[str] = None
+
+        # Accumulate text for current turn
+        self._current_turn_user_parts: List[TextPartForConcatenation] = []
+        self._current_turn_assistant_parts: List[TextPartForConcatenation] = []
+
+        # Track bot speaking state
+        self._bot_is_speaking = False
+
+        # Register turn events
+        self._register_event_handler("on_turn_started")
+        self._register_event_handler("on_turn_ended")
+
+    async def _start_turn(self):
+        """Start a new turn."""
+        if not self._turn_active:
+            self._turn_number += 1
+            self._turn_active = True
+            self._turn_start_time = time_now_iso8601()
+            self._current_turn_user_parts = []
+            self._current_turn_assistant_parts = []
+
+            logger.debug(f"Turn {self._turn_number} started")
+            await self._call_event_handler("on_turn_started", self._turn_number)
+
+    async def _end_turn(self, was_interrupted: bool = False):
+        """End the current turn and emit aggregated transcripts.
+
+        Args:
+            was_interrupted: Whether the turn ended due to an interruption.
+        """
+        if not self._turn_active:
+            return
+
+        # Aggregate user text
+        user_transcript = ""
+        if self._current_turn_user_parts:
+            user_transcript = concatenate_aggregated_text(self._current_turn_user_parts)
+
+        # Aggregate assistant text
+        assistant_transcript = ""
+        if self._current_turn_assistant_parts:
+            assistant_transcript = concatenate_aggregated_text(self._current_turn_assistant_parts)
+
+        # Emit turn ended event
+        logger.debug(
+            f"Turn {self._turn_number} ended (interrupted={was_interrupted}). "
+            f"User: '{user_transcript}', Assistant: '{assistant_transcript}'"
+        )
+        await self._call_event_handler(
+            "on_turn_ended",
+            self._turn_number,
+            user_transcript,
+            assistant_transcript,
+            was_interrupted,
+        )
+
+        # Reset turn state
+        self._turn_active = False
+        self._current_turn_user_parts = []
+        self._current_turn_assistant_parts = []
+
+    async def process_frame(self, frame: Frame, direction: FrameDirection):
+        """Process frames for turn-aware transcript tracking.
+
+        Handles:
+        - UserStartedSpeakingFrame: Start new turn
+        - TranscriptionFrame: Accumulate user speech and emit transcript message
+        - BotStartedSpeakingFrame: Track bot speaking state
+        - TTSTextFrame: Accumulate assistant speech
+        - BotStoppedSpeakingFrame: End turn if no interruption pending
+        - InterruptionFrame: End turn immediately as interrupted
+        - EndFrame/CancelFrame: End any active turn
+
+        Args:
+            frame: Input frame to process.
+            direction: Frame processing direction.
+        """
+        await super().process_frame(frame, direction)
+
+        if isinstance(frame, UserStartedSpeakingFrame):
+            # User started speaking
+            if self._bot_is_speaking:
+                # This is an interruption - end the current turn with what was spoken
+                if self._current_turn_assistant_parts:
+                    assistant_content = concatenate_aggregated_text(
+                        self._current_turn_assistant_parts
+                    )
+                    if assistant_content:
+                        message = TranscriptionMessage(
+                            role="assistant",
+                            content=assistant_content,
+                            timestamp=self._turn_start_time or time_now_iso8601(),
+                        )
+                        await self._emit_update([message])
+                await self._end_turn(was_interrupted=True)
+                self._bot_is_speaking = False
+            elif self._turn_active:
+                # Previous turn is ending normally (bot finished speaking)
+                if self._current_turn_assistant_parts:
+                    assistant_content = concatenate_aggregated_text(
+                        self._current_turn_assistant_parts
+                    )
+                    if assistant_content:
+                        message = TranscriptionMessage(
+                            role="assistant",
+                            content=assistant_content,
+                            timestamp=self._turn_start_time or time_now_iso8601(),
+                        )
+                        await self._emit_update([message])
+                await self._end_turn(was_interrupted=False)
+
+            # Start a new turn
+            await self._start_turn()
+            await self.push_frame(frame, direction)
+
+        elif isinstance(frame, TranscriptionFrame):
+            # Accumulate user speech for the current turn
+            if self._turn_active:
+                self._current_turn_user_parts.append(
+                    TextPartForConcatenation(frame.text, includes_inter_part_spaces=True)
+                )
+
+            # Also emit individual transcript message
+            message = TranscriptionMessage(
+                role="user",
+                user_id=frame.user_id,
+                content=frame.text,
+                timestamp=frame.timestamp,
+            )
+            await self._emit_update([message])
+            await self.push_frame(frame, direction)
+
+        elif isinstance(frame, BotStartedSpeakingFrame):
+            # Bot started speaking
+            self._bot_is_speaking = True
+            await self.push_frame(frame, direction)
+
+        elif isinstance(frame, TTSTextFrame):
+            # Accumulate assistant speech for the current turn
+            if self._turn_active:
+                self._current_turn_assistant_parts.append(
+                    TextPartForConcatenation(
+                        frame.text, includes_inter_part_spaces=frame.includes_inter_frame_spaces
+                    )
+                )
+            await self.push_frame(frame, direction)
+
+        elif isinstance(frame, BotStoppedSpeakingFrame):
+            # Bot stopped speaking - just mark it, don't end turn yet
+            # Turn will end when next user speaks or pipeline ends
+            self._bot_is_speaking = False
+            await self.push_frame(frame, direction)
+
+        elif isinstance(frame, InterruptionFrame):
+            # Emit assistant transcript message with what was spoken before interruption
+            if self._current_turn_assistant_parts:
+                assistant_content = concatenate_aggregated_text(self._current_turn_assistant_parts)
+                if assistant_content:
+                    message = TranscriptionMessage(
+                        role="assistant",
+                        content=assistant_content,
+                        timestamp=self._turn_start_time or time_now_iso8601(),
+                    )
+                    await self._emit_update([message])
+
+            # Push frame first to ensure proper cleanup
+            await self.push_frame(frame, direction)
+
+            # End turn as interrupted
+            await self._end_turn(was_interrupted=True)
+            self._bot_is_speaking = False
+
+        elif isinstance(frame, (EndFrame, CancelFrame)):
+            # Pipeline ending - finalize any active turn
+            if self._turn_active:
+                # Emit any pending assistant transcript (allow time for TTSTextFrames to be processed)
+                # Give a brief moment for any pending frames to process
+                import asyncio
+
+                await asyncio.sleep(0.001)
+
+                if self._current_turn_assistant_parts:
+                    assistant_content = concatenate_aggregated_text(
+                        self._current_turn_assistant_parts
+                    )
+                    if assistant_content:
+                        message = TranscriptionMessage(
+                            role="assistant",
+                            content=assistant_content,
+                            timestamp=self._turn_start_time or time_now_iso8601(),
+                        )
+                        await self._emit_update([message])
+
+                await self._end_turn(was_interrupted=isinstance(frame, CancelFrame))
+
+            await self.push_frame(frame, direction)
+
+        else:
+            await self.push_frame(frame, direction)

src/pipecat/runner/run.py

@@ -302,7 +302,7 @@ def _setup_webrtc_routes(
         result: StartBotResult = {"sessionId": session_id}
         if request_data.get("enableDefaultIceServers"):
             result["iceConfig"] = IceConfig(
-                iceServers=[IceServer(urls=["stun:stun.l.google.com:19302"])]
+                iceServers=[IceServer(urls="stun:stun.l.google.com:19302")]
             )
 
         return result

src/pipecat/serializers/plivo.py

@@ -199,7 +199,7 @@ class PlivoFrameSerializer(FrameSerializer):
                         )
 
         except Exception as e:
-            logger.error(f"Failed to hang up Plivo call: {e}")
+            logger.exception(f"Failed to hang up Plivo call: {e}")
 
     async def deserialize(self, data: str | bytes) -> Frame | None:
         """Deserializes Plivo WebSocket data to Pipecat frames.

src/pipecat/serializers/telnyx.py

@@ -225,7 +225,7 @@ class TelnyxFrameSerializer(FrameSerializer):
                         )
 
         except Exception as e:
-            logger.error(f"Failed to hang up Telnyx call: {e}")
+            logger.exception(f"Failed to hang up Telnyx call: {e}")
 
     async def deserialize(self, data: str | bytes) -> Frame | None:
         """Deserializes Telnyx WebSocket data to Pipecat frames.

src/pipecat/serializers/twilio.py

@@ -236,7 +236,7 @@ class TwilioFrameSerializer(FrameSerializer):
                         )
 
         except Exception as e:
-            logger.error(f"Failed to hang up Twilio call: {e}")
+            logger.exception(f"Failed to hang up Twilio call: {e}")
 
     async def deserialize(self, data: str | bytes) -> Frame | None:
         """Deserializes Twilio WebSocket data to Pipecat frames.

src/pipecat/services/ai_service.py

@@ -166,6 +166,6 @@ class AIService(FrameProcessor):
         async for f in generator:
             if f:
                 if isinstance(f, ErrorFrame):
-                    await self.push_error_frame(f)
+                    await self.push_error(f)
                 else:
                     await self.push_frame(f)

src/pipecat/services/anthropic/llm.py

@@ -458,7 +458,8 @@ class AnthropicLLMService(LLMService):
         except httpx.TimeoutException:
             await self._call_event_handler("on_completion_timeout")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(f"{e}"))
         finally:
             await self.stop_processing_metrics()
             await self.push_frame(LLMFullResponseEndFrame())

src/pipecat/services/assemblyai/stt.py

@@ -206,8 +206,9 @@ class AssemblyAISTTService(STTService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
+            logger.error(f"{self} exception: {e}")
             self._connected = False
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             raise
 
     async def _disconnect(self):
@@ -232,7 +233,8 @@ class AssemblyAISTTService(STTService):
                     logger.warning("Timed out waiting for termination message from server")
 
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
             if self._receive_task:
                 await self.cancel_task(self._receive_task)
@@ -240,7 +242,8 @@ class AssemblyAISTTService(STTService):
             await self._websocket.close()
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
         finally:
             self._websocket = None
@@ -259,11 +262,13 @@ class AssemblyAISTTService(STTService):
                 except websockets.exceptions.ConnectionClosedOK:
                     break
                 except Exception as e:
-                    await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                    logger.error(f"{self} exception: {e}")
+                    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                     break
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     def _parse_message(self, message: Dict[str, Any]) -> BaseMessage:
         """Parse a raw message into the appropriate message type."""
@@ -292,7 +297,8 @@ class AssemblyAISTTService(STTService):
             elif isinstance(parsed_message, TerminationMessage):
                 await self._handle_termination(parsed_message)
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def _handle_termination(self, message: TerminationMessage):
         """Handle termination message."""

src/pipecat/services/asyncai/tts.py

@@ -56,17 +56,6 @@ def language_to_async_language(language: Language) -> Optional[str]:
         Language.ES: "es",
         Language.DE: "de",
         Language.IT: "it",
-        Language.PT: "pt",
-        Language.NL: "nl",
-        Language.AR: "ar",
-        Language.RU: "ru",
-        Language.RO: "ro",
-        Language.JA: "ja",
-        Language.HE: "he",
-        Language.HY: "hy",
-        Language.TR: "tr",
-        Language.HI: "hi",
-        Language.ZH: "zh",
     }
 
     return resolve_language(language, LANGUAGE_MAP, use_base_code=True)
@@ -85,7 +74,7 @@ class AsyncAITTSService(InterruptibleTTSService):
             language: Language to use for synthesis.
         """
 
-        language: Optional[Language] = None
+        language: Optional[Language] = Language.EN
 
     def __init__(
         self,
@@ -94,7 +83,7 @@ class AsyncAITTSService(InterruptibleTTSService):
         voice_id: str,
         version: str = "v1",
         url: str = "wss://api.async.ai/text_to_speech/websocket/ws",
-        model: str = "asyncflow_multilingual_v1.0",
+        model: str = "asyncflow_v2.0",
         sample_rate: Optional[int] = None,
         encoding: str = "pcm_s16le",
         container: str = "raw",
@@ -110,7 +99,7 @@ class AsyncAITTSService(InterruptibleTTSService):
                 https://docs.async.ai/list-voices-16699698e0
             version: Async API version.
             url: WebSocket URL for Async TTS API.
-            model: TTS model to use (e.g., "asyncflow_multilingual_v1.0").
+            model: TTS model to use (e.g., "asyncflow_v2.0").
             sample_rate: Audio sample rate.
             encoding: Audio encoding format.
             container: Audio container format.
@@ -139,7 +128,7 @@ class AsyncAITTSService(InterruptibleTTSService):
             },
             "language": self.language_to_service_language(params.language)
             if params.language
-            else None,
+            else "en",
         }
 
         self.set_model_name(model)
@@ -239,7 +228,8 @@ class AsyncAITTSService(InterruptibleTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -251,7 +241,8 @@ class AsyncAITTSService(InterruptibleTTSService):
                 logger.debug("Disconnecting from Async")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._websocket = None
             self._started = False
@@ -296,11 +287,12 @@ class AsyncAITTSService(InterruptibleTTSService):
                 )
                 await self.push_frame(frame)
             elif msg.get("error_code"):
+                logger.error(f"{self} error: {msg}")
                 await self.push_frame(TTSStoppedFrame())
                 await self.stop_all_metrics()
-                await self.push_error(error_msg=f"Error: {msg['message']}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {msg['message']}"))
             else:
-                await self.push_error(error_msg=f"Unknown message type: {msg}")
+                logger.error(f"{self} error, unknown message type: {msg}")
 
     async def _keepalive_task_handler(self):
         """Send periodic keepalive messages to maintain WebSocket connection."""
@@ -343,14 +335,16 @@ class AsyncAITTSService(InterruptibleTTSService):
                 await self._get_websocket().send(msg)
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class AsyncAIHttpTTSService(TTSService):
@@ -368,7 +362,7 @@ class AsyncAIHttpTTSService(TTSService):
             language: Language to use for synthesis.
         """
 
-        language: Optional[Language] = None
+        language: Optional[Language] = Language.EN
 
     def __init__(
         self,
@@ -376,7 +370,7 @@ class AsyncAIHttpTTSService(TTSService):
         api_key: str,
         voice_id: str,
         aiohttp_session: aiohttp.ClientSession,
-        model: str = "asyncflow_multilingual_v1.0",
+        model: str = "asyncflow_v2.0",
         url: str = "https://api.async.ai",
         version: str = "v1",
         sample_rate: Optional[int] = None,
@@ -391,7 +385,7 @@ class AsyncAIHttpTTSService(TTSService):
             api_key: Async API key.
             voice_id: ID of the voice to use for synthesis.
             aiohttp_session: An aiohttp session for making HTTP requests.
-            model: TTS model to use (e.g., "asyncflow_multilingual_v1.0").
+            model: TTS model to use (e.g., "asyncflow_v2.0").
             url: Base URL for Async API.
             version: API version string for Async API.
             sample_rate: Audio sample rate.
@@ -415,7 +409,7 @@ class AsyncAIHttpTTSService(TTSService):
             },
             "language": self.language_to_service_language(params.language)
             if params.language
-            else None,
+            else "en",
         }
         self.set_voice(voice_id)
         self.set_model_name(model)
@@ -483,7 +477,8 @@ class AsyncAIHttpTTSService(TTSService):
             async with self._session.post(url, json=payload, headers=headers) as response:
                 if response.status != 200:
                     error_text = await response.text()
-                    await self.push_error(error_msg=f"Async API error: {error_text}")
+                    logger.error(f"Async API error: {error_text}")
+                    await self.push_error(ErrorFrame(error=f"Async API error: {error_text}"))
                     raise Exception(f"Async API returned status {response.status}: {error_text}")
 
                 audio_data = await response.read()
@@ -499,7 +494,8 @@ class AsyncAIHttpTTSService(TTSService):
             yield frame
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/aws/llm.py

@@ -734,7 +734,7 @@ class AWSBedrockLLMService(LLMService):
         aws_access_key: Optional[str] = None,
         aws_secret_key: Optional[str] = None,
         aws_session_token: Optional[str] = None,
-        aws_region: Optional[str] = None,
+        aws_region: str = "us-east-1",
         params: Optional[InputParams] = None,
         client_config: Optional[Config] = None,
         retry_timeout_secs: Optional[float] = 5.0,
@@ -1136,7 +1136,7 @@ class AWSBedrockLLMService(LLMService):
         except (ReadTimeoutError, asyncio.TimeoutError):
             await self._call_event_handler("on_completion_timeout")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} exception: {e}")
         finally:
             await self.stop_processing_metrics()
             await self.push_frame(LLMFullResponseEndFrame())

src/pipecat/services/aws/nova_sonic/llm.py

@@ -453,7 +453,7 @@ class AWSNovaSonicLLMService(LLMService):
             self._ready_to_send_context = True
             await self._finish_connecting_if_context_available()
         except Exception as e:
-            await self.push_error(error_msg=f"Initialization error: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             await self._disconnect()
 
     async def _process_completed_function_calls(self, send_new_results: bool):
@@ -577,7 +577,7 @@ class AWSNovaSonicLLMService(LLMService):
 
             logger.info("Finished disconnecting")
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} error disconnecting: {e}")
 
     def _create_client(self) -> BedrockRuntimeClient:
         config = Config(
@@ -885,7 +885,7 @@ class AWSNovaSonicLLMService(LLMService):
                 # Errors are kind of expected while disconnecting, so just
                 # ignore them and do nothing
                 return
-            await self.push_error(error_msg=f"Error processing responses: {e}", exception=e)
+            logger.error(f"{self} error processing responses: {e}")
             if self._wants_connection:
                 await self.reset_conversation()
 

src/pipecat/services/aws/stt.py

@@ -58,7 +58,7 @@ class AWSTranscribeSTTService(STTService):
         api_key: Optional[str] = None,
         aws_access_key_id: Optional[str] = None,
         aws_session_token: Optional[str] = None,
-        region: Optional[str] = None,
+        region: Optional[str] = "us-east-1",
         sample_rate: int = 16000,
         language: Language = Language.EN,
         **kwargs,
@@ -69,7 +69,7 @@ class AWSTranscribeSTTService(STTService):
             api_key: AWS secret access key. If None, uses AWS_SECRET_ACCESS_KEY environment variable.
             aws_access_key_id: AWS access key ID. If None, uses AWS_ACCESS_KEY_ID environment variable.
             aws_session_token: AWS session token for temporary credentials. If None, uses AWS_SESSION_TOKEN environment variable.
-            region: AWS region for the service.
+            region: AWS region for the service. Defaults to "us-east-1".
             sample_rate: Audio sample rate in Hz. Must be 8000 or 16000. Defaults to 16000.
             language: Language for transcription. Defaults to English.
             **kwargs: Additional arguments passed to parent STTService class.
@@ -140,7 +140,8 @@ class AWSTranscribeSTTService(STTService):
                     return
                 logger.warning("WebSocket connection not established after connect")
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                 retry_count += 1
                 if retry_count < max_retries:
                     await asyncio.sleep(1)  # Wait before retrying
@@ -181,7 +182,8 @@ class AWSTranscribeSTTService(STTService):
                 try:
                     await self._connect()
                 except Exception as e:
-                    yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                    logger.error(f"{self} exception: {e}")
+                    yield ErrorFrame(error=f"{self} error: {e}")
                     return
 
             # Format the audio data according to AWS event stream format
@@ -198,11 +200,13 @@ class AWSTranscribeSTTService(STTService):
                 await self._disconnect()
                 # Don't yield error here - we'll retry on next frame
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 await self._disconnect()
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
             await self._disconnect()
 
     async def _connect(self):
@@ -285,7 +289,8 @@ class AWSTranscribeSTTService(STTService):
 
                 await self._call_event_handler("on_connected")
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                 await self._disconnect()
                 raise
 
@@ -305,7 +310,8 @@ class AWSTranscribeSTTService(STTService):
                 await self._ws_client.send(json.dumps(end_stream))
             await self._ws_client.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._ws_client = None
             await self._call_event_handler("on_disconnected")
@@ -523,15 +529,15 @@ class AWSTranscribeSTTService(STTService):
                                     )
                 elif headers.get(":message-type") == "exception":
                     error_msg = payload.get("Message", "Unknown error")
-                    await self.push_error(error_msg=f"AWS Transcribe error: {error_msg}")
+                    logger.error(f"{self} Exception from AWS: {error_msg}")
+                    await self.push_frame(ErrorFrame(f"AWS Transcribe error: {error_msg}"))
                 else:
                     logger.debug(f"{self} Other message type received: {headers}")
                     logger.debug(f"{self} Payload: {payload}")
             except websockets.exceptions.ConnectionClosed as e:
-                await self.push_error(
-                    error_msg=f"WebSocket connection closed in receive loop", exception=e
-                )
+                logger.error(f"{self} WebSocket connection closed in receive loop: {e}")
                 break
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                 break

src/pipecat/services/aws/tts.py

@@ -312,6 +312,7 @@ class AWSPollyTTSService(TTSService):
 
                 yield TTSStoppedFrame()
         except (BotoCoreError, ClientError) as error:
+            logger.exception(f"{self} error generating TTS: {error}")
             error_message = f"AWS Polly TTS error: {str(error)}"
             yield ErrorFrame(error=error_message)
 

src/pipecat/services/azure/image.py

@@ -91,6 +91,7 @@ class AzureImageGenServiceREST(ImageGenService):
             while status != "succeeded":
                 attempts_left -= 1
                 if attempts_left == 0:
+                    logger.error(f"{self} error: image generation timed out")
                     yield ErrorFrame("Image generation timed out")
                     return
 
@@ -103,6 +104,7 @@ class AzureImageGenServiceREST(ImageGenService):
 
             image_url = json_response["result"]["data"][0]["url"] if json_response else None
             if not image_url:
+                logger.error(f"{self} error: image generation failed")
                 yield ErrorFrame("Image generation failed")
                 return
 

src/pipecat/services/azure/realtime/llm.py

@@ -61,5 +61,5 @@ class AzureRealtimeLLMService(OpenAIRealtimeLLMService):
             )
             self._receive_task = self.create_task(self._receive_task_handler())
         except Exception as e:
-            await self.push_error(error_msg=f"initialization error: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             self._websocket = None

src/pipecat/services/azure/stt.py

@@ -121,7 +121,8 @@ class AzureSTTService(STTService):
                 self._audio_stream.write(audio)
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
     async def start(self, frame: StartFrame):
         """Start the speech recognition service.
@@ -150,9 +151,8 @@ class AzureSTTService(STTService):
             self._speech_recognizer.recognized.connect(self._on_handle_recognized)
             self._speech_recognizer.start_continuous_recognition_async()
         except Exception as e:
-            await self.push_error(
-                error_msg=f"Uncaught exception during initialization: {e}", exception=e
-            )
+            logger.error(f"{self} exception during initialization: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def stop(self, frame: EndFrame):
         """Stop the speech recognition service.

src/pipecat/services/azure/tts.py

@@ -327,6 +327,7 @@ class AzureTTSService(AzureBaseTTSService):
         try:
             if self._speech_synthesizer is None:
                 error_msg = "Speech synthesizer not initialized."
+                logger.error(error_msg)
                 yield ErrorFrame(error=error_msg)
                 return
 
@@ -354,13 +355,15 @@ class AzureTTSService(AzureBaseTTSService):
                 yield TTSStoppedFrame()
 
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 # Could add reconnection logic here if needed
                 return
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class AzureHttpTTSService(AzureBaseTTSService):
@@ -437,6 +440,5 @@ class AzureHttpTTSService(AzureBaseTTSService):
             cancellation_details = result.cancellation_details
             logger.warning(f"Speech synthesis canceled: {cancellation_details.reason}")
             if cancellation_details.reason == CancellationReason.Error:
-                yield ErrorFrame(
-                    error=f"Unknown error occurred: {cancellation_details.error_details}"
-                )
+                logger.error(f"{self} error: {cancellation_details.error_details}")
+                yield ErrorFrame(error=f"{self} error: {cancellation_details.error_details}")

src/pipecat/services/cartesia/stt.py

@@ -20,6 +20,7 @@ from loguru import logger
 from pipecat.frames.frames import (
     CancelFrame,
     EndFrame,
+    ErrorFrame,
     Frame,
     InterimTranscriptionFrame,
     StartFrame,
@@ -275,7 +276,8 @@ class CartesiaSTTService(WebsocketSTTService):
             self._websocket = await websocket_connect(ws_url, additional_headers=headers)
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def _disconnect_websocket(self):
         try:
@@ -283,7 +285,8 @@ class CartesiaSTTService(WebsocketSTTService):
                 logger.debug("Disconnecting from Cartesia STT")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error closing websocket: {e}", exception=e)
+            logger.error(f"{self} error closing websocket: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._websocket = None
             await self._call_event_handler("on_disconnected")
@@ -316,7 +319,8 @@ class CartesiaSTTService(WebsocketSTTService):
 
             elif data["type"] == "error":
                 error_msg = data.get("message", "Unknown error")
-                await self.push_error(error_msg=error_msg)
+                logger.error(f"Cartesia error: {error_msg}")
+                await self.push_error(ErrorFrame(error=error_msg))
 
     @traced_stt
     async def _handle_transcription(
@@ -348,7 +352,6 @@ class CartesiaSTTService(WebsocketSTTService):
                         self._user_id,
                         time_now_iso8601(),
                         language,
-                        result=data,
                     )
                 )
                 await self._handle_transcription(transcript, is_final, language)
@@ -361,6 +364,5 @@ class CartesiaSTTService(WebsocketSTTService):
                         self._user_id,
                         time_now_iso8601(),
                         language,
-                        result=data,
                     )
                 )

src/pipecat/services/cartesia/tts.py

@@ -497,7 +497,8 @@ class CartesiaTTSService(AudioContextWordTTSService):
             )
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -509,7 +510,8 @@ class CartesiaTTSService(AudioContextWordTTSService):
                 logger.debug("Disconnecting from Cartesia")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._context_id = None
             self._websocket = None
@@ -562,12 +564,13 @@ class CartesiaTTSService(AudioContextWordTTSService):
                 )
                 await self.append_to_audio_context(msg["context_id"], frame)
             elif msg["type"] == "error":
+                logger.error(f"{self} error: {msg}")
                 await self.push_frame(TTSStoppedFrame())
                 await self.stop_all_metrics()
-                await self.push_error(error_msg=f"Error: {msg}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {msg['error']}"))
                 self._context_id = None
             else:
-                await self.push_error(error_msg=f"Error, unknown message type: {msg}")
+                logger.error(f"{self} error, unknown message type: {msg}")
 
     async def _receive_messages(self):
         while True:
@@ -605,14 +608,16 @@ class CartesiaTTSService(AudioContextWordTTSService):
                 await self._get_websocket().send(msg)
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class CartesiaHttpTTSService(TTSService):
@@ -803,7 +808,8 @@ class CartesiaHttpTTSService(TTSService):
             async with session.post(url, json=payload, headers=headers) as response:
                 if response.status != 200:
                     error_text = await response.text()
-                    yield ErrorFrame(error=f"Cartesia API error: {error_text}")
+                    logger.error(f"Cartesia API error: {error_text}")
+                    await self.push_error(ErrorFrame(error=f"Cartesia API error: {error_text}"))
                     raise Exception(f"Cartesia API returned status {response.status}: {error_text}")
 
                 audio_data = await response.read()
@@ -819,7 +825,8 @@ class CartesiaHttpTTSService(TTSService):
             yield frame
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/deepgram/flux/stt.py

@@ -150,17 +150,7 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                     params=params
                 )
         """
-        # Note: For DeepgramFluxSTTService, differently from other processes, we need to create
-        # the _receive_task inside _connect_websocket, because the websocket should only be
-        # considered connected and ready to send audio once we receive from Flux the message
-        # which confirms the connection has been established.
-        # If we try to keep the logic reconnect_on_error, when receiving a message, the
-        # _receive_task_handler would try to reconnect in case of error, invoking the
-        # _connect_websocket again and leading to a case where the first _receive_task_handler
-        # was never destroyed.
-        # So we can keep it here as false, because inside the method send_with_retry, it will
-        # already try to reconnect if needed.
-        super().__init__(sample_rate=sample_rate, reconnect_on_error=False, **kwargs)
+        super().__init__(sample_rate=sample_rate, **kwargs)
 
         self._api_key = api_key
         self._url = url
@@ -193,6 +183,14 @@ class DeepgramFluxSTTService(WebsocketSTTService):
         """
         await self._connect_websocket()
 
+        # Creating the receiver task (only created once during initial connection)
+        if not self._receive_task:
+            self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
+
+        # Creating the watchdog task (only created once during initial connection)
+        if not self._watchdog_task:
+            self._watchdog_task = self.create_task(self._watchdog_task_handler())
+
     async def _disconnect(self):
         """Disconnect from WebSocket and clean up tasks.
 
@@ -202,7 +200,8 @@ class DeepgramFluxSTTService(WebsocketSTTService):
         try:
             await self._disconnect_websocket()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             # Reset state only after everything is cleaned up
             self._websocket = None
@@ -244,28 +243,14 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                 additional_headers={"Authorization": f"Token {self._api_key}"},
             )
 
-            headers = {
-                k: v for k, v in self._websocket.response.headers.items() if k.startswith("dg-")
-            }
-            logger.debug(f'{self}: Websocket connection initialized: {{"headers": {headers}}}')
-
-            # Creating the receiver task
-            if not self._receive_task:
-                self._receive_task = self.create_task(
-                    self._receive_task_handler(self._report_error)
-                )
-
-            # Creating the watchdog task
-            if not self._watchdog_task:
-                self._watchdog_task = self.create_task(self._watchdog_task_handler())
-
             # Now wait for the connection established event
             logger.debug("WebSocket connected, waiting for server confirmation...")
             await self._connection_established_event.wait()
             logger.debug("Connected to Deepgram Flux Websocket")
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -293,7 +278,8 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                 logger.debug("Disconnecting from Deepgram Flux Websocket")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error closing websocket: {e}", exception=e)
+            logger.error(f"{self} error closing websocket: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._websocket = None
             await self._call_event_handler("on_disconnected")
@@ -303,13 +289,10 @@ class DeepgramFluxSTTService(WebsocketSTTService):
 
         This signals to the server that no more audio data will be sent.
         """
-        try:
-            if self._websocket:
-                logger.debug("Sending CloseStream message to Deepgram Flux")
-                message = {"type": "CloseStream"}
-                await self._websocket.send(json.dumps(message))
-        except Exception as e:
-            await self.push_error(error_msg=f"Error sending closeStream: {e}", exception=e)
+        if self._websocket:
+            logger.debug("Sending CloseStream message to Deepgram Flux")
+            message = {"type": "CloseStream"}
+            await self._websocket.send(json.dumps(message))
 
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate processing metrics.
@@ -396,13 +379,16 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                 are issues sending the audio data.
         """
         if not self._websocket:
+            logger.error("Not connected to Deepgram Flux.")
+            yield ErrorFrame("Not connected to Deepgram Flux.")
             return
 
         try:
             self._last_stt_time = time.monotonic()
             await self.send_with_retry(audio, self._report_error)
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
             return
 
         yield None
@@ -479,7 +465,8 @@ class DeepgramFluxSTTService(WebsocketSTTService):
                     # Skip malformed messages
                     continue
                 except Exception as e:
-                    await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                    logger.error(f"{self} exception: {e}")
+                    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                     # Error will be handled inside WebsocketService->_receive_task_handler
                     raise
             else:

src/pipecat/services/deepgram/stt.py

@@ -233,14 +233,7 @@ class DeepgramSTTService(STTService):
             )
 
         if not await self._connection.start(options=self._settings, addons=self._addons):
-            await self.push_error(error_msg=f"Unable to connect to Deepgram")
-        else:
-            headers = {
-                k: v
-                for k, v in self._connection._socket.response.headers.items()
-                if k.startswith("dg-")
-            }
-            logger.debug(f'{self}: Websocket connection initialized: {{"headers": {headers}}}')
+            logger.error(f"{self}: unable to connect to Deepgram")
 
     async def _disconnect(self):
         if await self._connection.is_connected():
@@ -263,7 +256,7 @@ class DeepgramSTTService(STTService):
     async def _on_error(self, *args, **kwargs):
         error: ErrorResponse = kwargs["error"]
         logger.warning(f"{self} connection error, will retry: {error}")
-        await self.push_error(error_msg=f"{error}")
+        await self.push_error(ErrorFrame(error=f"{error}"))
         await self.stop_all_metrics()
         # NOTE(aleix): we don't disconnect (i.e. call finish on the connection)
         # because this triggers more errors internally in the Deepgram SDK. So,

src/pipecat/services/deepgram/stt_sagemaker.py

@@ -210,7 +210,8 @@ class DeepgramSageMakerSTTService(STTService):
             try:
                 await self._client.send_audio_chunk(audio)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"Error sending audio to SageMaker: {e}")
+                await self.push_error(ErrorFrame(error=f"SageMaker STT error: {e}"))
         yield None
 
     async def _connect(self):
@@ -259,7 +260,8 @@ class DeepgramSageMakerSTTService(STTService):
             await self._call_event_handler("on_connected")
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"Failed to connect to SageMaker: {e}")
+            await self.push_error(ErrorFrame(error=f"SageMaker connection error: {e}"))
             await self._call_event_handler("on_connection_error", str(e))
 
     async def _disconnect(self):
@@ -340,7 +342,8 @@ class DeepgramSageMakerSTTService(STTService):
         except asyncio.CancelledError:
             logger.debug("Response processor cancelled")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"Error processing responses: {e}", exc_info=True)
+            await self.push_error(ErrorFrame(error=f"SageMaker response error: {e}"))
         finally:
             logger.debug("Response processor stopped")
 

src/pipecat/services/deepgram/tts.py

@@ -10,45 +10,35 @@ This module provides integration with Deepgram's text-to-speech API
 for generating speech from text using various voice models.
 """
 
-import json
 from typing import AsyncGenerator, Optional
 
 import aiohttp
 from loguru import logger
 
 from pipecat.frames.frames import (
-    CancelFrame,
-    EndFrame,
     ErrorFrame,
     Frame,
-    InterruptionFrame,
-    LLMFullResponseEndFrame,
-    StartFrame,
     TTSAudioRawFrame,
     TTSStartedFrame,
     TTSStoppedFrame,
 )
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.tts_service import TTSService, WebsocketTTSService
+from pipecat.services.tts_service import TTSService
 from pipecat.utils.tracing.service_decorators import traced_tts
 
 try:
-    from websockets.asyncio.client import connect as websocket_connect
-    from websockets.protocol import State
+    from deepgram import DeepgramClient, DeepgramClientOptions, SpeakOptions
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
-    logger.error(
-        "In order to use DeepgramWebsocketTTSService, you need to `pip install pipecat-ai[deepgram]`."
-    )
+    logger.error("In order to use Deepgram, you need to `pip install pipecat-ai[deepgram]`.")
     raise Exception(f"Missing module: {e}")
 
 
-class DeepgramTTSService(WebsocketTTSService):
-    """Deepgram WebSocket-based text-to-speech service.
+class DeepgramTTSService(TTSService):
+    """Deepgram text-to-speech service.
 
-    Provides real-time text-to-speech synthesis using Deepgram's WebSocket API.
-    Supports streaming audio generation with interruption handling via the Clear
-    message for conversational AI use cases.
+    Provides text-to-speech synthesis using Deepgram's streaming API.
+    Supports various voice models and audio encoding formats with
+    configurable sample rates and quality settings.
     """
 
     def __init__(
@@ -56,220 +46,42 @@ class DeepgramTTSService(WebsocketTTSService):
         *,
         api_key: str,
         voice: str = "aura-2-helena-en",
-        base_url: str = "wss://api.deepgram.com",
+        base_url: str = "",
         sample_rate: Optional[int] = None,
         encoding: str = "linear16",
         **kwargs,
     ):
-        """Initialize the Deepgram WebSocket TTS service.
+        """Initialize the Deepgram TTS service.
 
         Args:
             api_key: Deepgram API key for authentication.
             voice: Voice model to use for synthesis. Defaults to "aura-2-helena-en".
-            base_url: WebSocket base URL for Deepgram API. Defaults to "wss://api.deepgram.com".
+            base_url: Custom base URL for Deepgram API. Uses default if empty.
             sample_rate: Audio sample rate in Hz. If None, uses service default.
             encoding: Audio encoding format. Defaults to "linear16".
-            **kwargs: Additional arguments passed to parent InterruptibleTTSService class.
+            **kwargs: Additional arguments passed to parent TTSService class.
         """
-        super().__init__(
-            sample_rate=sample_rate,
-            pause_frame_processing=True,
-            push_stop_frames=True,
-            **kwargs,
-        )
+        super().__init__(sample_rate=sample_rate, **kwargs)
 
-        self._api_key = api_key
-        self._base_url = base_url
         self._settings = {
             "encoding": encoding,
         }
         self.set_voice(voice)
 
-        self._receive_task = None
+        client_options = DeepgramClientOptions(url=base_url)
+        self._deepgram_client = DeepgramClient(api_key, config=client_options)
 
     def can_generate_metrics(self) -> bool:
         """Check if the service can generate metrics.
 
         Returns:
-            True, as Deepgram WebSocket TTS service supports metrics generation.
+            True, as Deepgram TTS service supports metrics generation.
         """
         return True
 
-    async def start(self, frame: StartFrame):
-        """Start the Deepgram WebSocket TTS service.
-
-        Args:
-            frame: The start frame containing initialization parameters.
-        """
-        await super().start(frame)
-        await self._connect()
-
-    async def stop(self, frame: EndFrame):
-        """Stop the Deepgram WebSocket TTS service.
-
-        Args:
-            frame: The end frame.
-        """
-        await super().stop(frame)
-        await self._disconnect()
-
-    async def cancel(self, frame: CancelFrame):
-        """Cancel the Deepgram WebSocket TTS service.
-
-        Args:
-            frame: The cancel frame.
-        """
-        await super().cancel(frame)
-        await self._disconnect()
-
-    async def process_frame(self, frame: Frame, direction: FrameDirection):
-        """Process frames with special handling for LLM response end.
-
-        Args:
-            frame: The frame to process.
-            direction: The direction of frame processing.
-        """
-        await super().process_frame(frame, direction)
-
-        # When the LLM finishes responding, flush any remaining text in Deepgram's buffer
-        if isinstance(frame, (LLMFullResponseEndFrame, EndFrame)):
-            await self.flush_audio()
-
-    async def _connect(self):
-        """Connect to Deepgram WebSocket and start receive task."""
-        await self._connect_websocket()
-
-        if self._websocket and not self._receive_task:
-            self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
-
-    async def _disconnect(self):
-        """Disconnect from Deepgram WebSocket and clean up tasks."""
-        if self._receive_task:
-            await self.cancel_task(self._receive_task)
-            self._receive_task = None
-
-        await self._disconnect_websocket()
-
-    async def _connect_websocket(self):
-        """Connect to Deepgram WebSocket API with configured settings."""
-        try:
-            if self._websocket and self._websocket.state is State.OPEN:
-                return
-
-            logger.debug("Connecting to Deepgram WebSocket")
-
-            # Build WebSocket URL with query parameters
-            params = []
-            params.append(f"model={self._voice_id}")
-            params.append(f"encoding={self._settings['encoding']}")
-            params.append(f"sample_rate={self.sample_rate}")
-
-            url = f"{self._base_url}/v1/speak?{'&'.join(params)}"
-
-            headers = {"Authorization": f"Token {self._api_key}"}
-
-            self._websocket = await websocket_connect(url, additional_headers=headers)
-
-            headers = {
-                k: v for k, v in self._websocket.response.headers.items() if k.startswith("dg-")
-            }
-            logger.debug(f'{self}: Websocket connection initialized: {{"headers": {headers}}}')
-
-            await self._call_event_handler("on_connected")
-        except Exception as e:
-            logger.error(f"{self} exception: {e}")
-            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
-            self._websocket = None
-            await self._call_event_handler("on_connection_error", f"{e}")
-
-    async def _disconnect_websocket(self):
-        """Close WebSocket connection and reset state."""
-        try:
-            await self.stop_all_metrics()
-
-            if self._websocket:
-                logger.debug("Disconnecting from Deepgram WebSocket")
-                # Send Close message to gracefully close the connection
-                await self._websocket.send(json.dumps({"type": "Close"}))
-                await self._websocket.close()
-        except Exception as e:
-            logger.error(f"{self} exception: {e}")
-            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
-        finally:
-            self._websocket = None
-            await self._call_event_handler("on_disconnected")
-
-    def _get_websocket(self):
-        """Get active websocket connection or raise exception."""
-        if self._websocket:
-            return self._websocket
-        raise Exception("Websocket not connected")
-
-    async def _handle_interruption(self, frame: InterruptionFrame, direction: FrameDirection):
-        """Handle interruption by sending Clear message to Deepgram.
-
-        The Clear message will clear Deepgram's internal text buffer and stop
-        sending audio, allowing for a new response to be generated.
-        """
-        await super()._handle_interruption(frame, direction)
-
-        # Send Clear message to stop current audio generation
-        if self._websocket:
-            try:
-                clear_msg = {"type": "Clear"}
-                await self._websocket.send(json.dumps(clear_msg))
-            except Exception as e:
-                logger.error(f"{self} error sending Clear message: {e}")
-
-    async def _receive_messages(self):
-        """Receive and process messages from Deepgram WebSocket."""
-        async for message in self._get_websocket():
-            if isinstance(message, bytes):
-                # Binary message contains audio data
-                await self.stop_ttfb_metrics()
-                frame = TTSAudioRawFrame(message, self.sample_rate, 1)
-                await self.push_frame(frame)
-            elif isinstance(message, str):
-                # Text message contains metadata or control messages
-                try:
-                    msg = json.loads(message)
-                    msg_type = msg.get("type")
-
-                    if msg_type == "Metadata":
-                        logger.trace(f"Received metadata: {msg}")
-                    elif msg_type == "Flushed":
-                        logger.trace(f"Received Flushed: {msg}")
-                        # Flushed indicates the end of audio generation for the current buffer
-                        # This happens after flush_audio() is called
-                    elif msg_type == "Cleared":
-                        logger.trace(f"Received Cleared: {msg}")
-                        # Buffer has been cleared after interruption
-                        # TTSStoppedFrame will be sent by the interruption handler
-                    elif msg_type == "Warning":
-                        logger.warning(
-                            f"{self} warning: {msg.get('description', 'Unknown warning')}"
-                        )
-                    else:
-                        logger.debug(f"Received unknown message type: {msg}")
-                except json.JSONDecodeError:
-                    logger.error(f"Invalid JSON message: {message}")
-
-    async def flush_audio(self):
-        """Flush any pending audio synthesis by sending Flush command.
-
-        This should be called when the LLM finishes a complete response to force
-        generation of audio from Deepgram's internal text buffer.
-        """
-        if self._websocket:
-            try:
-                flush_msg = {"type": "Flush"}
-                await self._websocket.send(json.dumps(flush_msg))
-            except Exception as e:
-                logger.error(f"{self} error sending Flush message: {e}")
-
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        """Generate speech from text using Deepgram's WebSocket TTS API.
+        """Generate speech from text using Deepgram's TTS API.
 
         Args:
             text: The text to synthesize into speech.
@@ -279,27 +91,33 @@ class DeepgramTTSService(WebsocketTTSService):
         """
         logger.debug(f"{self}: Generating TTS [{text}]")
 
+        options = SpeakOptions(
+            model=self._voice_id,
+            encoding=self._settings["encoding"],
+            sample_rate=self.sample_rate,
+            container="none",
+        )
+
         try:
-            # Reconnect if the websocket is closed
-            if not self._websocket or self._websocket.state is State.CLOSED:
-                await self._connect()
-
             await self.start_ttfb_metrics()
-            await self.start_tts_usage_metrics(text)
 
+            response = await self._deepgram_client.speak.asyncrest.v("1").stream_raw(
+                {"text": text}, options
+            )
+
+            await self.start_tts_usage_metrics(text)
             yield TTSStartedFrame()
 
-            # Send text message to Deepgram
-            # Note: We don't send Flush here - that should only be sent when the
-            # LLM finishes a complete response via flush_audio()
-            speak_msg = {"type": "Speak", "text": text}
-            await self._get_websocket().send(json.dumps(speak_msg))
+            async for data in response.aiter_bytes():
+                await self.stop_ttfb_metrics()
+                if data:
+                    yield TTSAudioRawFrame(audio=data, sample_rate=self.sample_rate, num_channels=1)
 
-            # The audio frames will be handled in _receive_messages
-            yield None
+            yield TTSStoppedFrame()
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class DeepgramHttpTTSService(TTSService):
@@ -409,4 +227,5 @@ class DeepgramHttpTTSService(TTSService):
             yield TTSStoppedFrame()
 
         except Exception as e:
+            logger.exception(f"{self} exception: {e}")
             yield ErrorFrame(f"Error getting audio: {str(e)}")

src/pipecat/services/elevenlabs/stt.py

@@ -351,7 +351,8 @@ class ElevenLabsSTTService(SegmentedSTTService):
                 )
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 def audio_format_from_sample_rate(sample_rate: int) -> str:
@@ -597,6 +598,7 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
                 }
                 await self._websocket.send(json.dumps(message))
             except Exception as e:
+                logger.error(f"Error sending audio: {e}")
                 yield ErrorFrame(f"ElevenLabs Realtime STT error: {str(e)}")
 
         yield None
@@ -661,9 +663,8 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
             await self._call_event_handler("on_connected")
             logger.debug("Connected to ElevenLabs Realtime STT")
         except Exception as e:
-            await self.push_error(
-                error_msg=f"Unable to connect to ElevenLabs Realtime STT: {e}", exception=e
-            )
+            logger.error(f"{self}: unable to connect to ElevenLabs Realtime STT: {e}")
+            await self.push_error(ErrorFrame(f"Connection error: {str(e)}"))
 
     async def _disconnect_websocket(self):
         """Disconnect from ElevenLabs Realtime STT WebSocket."""
@@ -672,7 +673,7 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
                 logger.debug("Disconnecting from ElevenLabs Realtime STT")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error closing websocket: {e}", exception=e)
+            logger.error(f"{self} error closing websocket: {e}")
         finally:
             self._websocket = None
             await self._call_event_handler("on_disconnected")
@@ -732,17 +733,17 @@ class ElevenLabsRealtimeSTTService(WebsocketSTTService):
         elif message_type == "error":
             error_msg = data.get("error", "Unknown error")
             logger.error(f"ElevenLabs error: {error_msg}")
-            await self.push_error(error_msg=f"Error: {error_msg}")
+            await self.push_error(ErrorFrame(f"Error: {error_msg}"))
 
         elif message_type == "auth_error":
             error_msg = data.get("error", "Authentication error")
             logger.error(f"ElevenLabs auth error: {error_msg}")
-            await self.push_error(error_msg=f"Auth error: {error_msg}")
+            await self.push_error(ErrorFrame(f"Auth error: {error_msg}"))
 
         elif message_type == "quota_exceeded_error":
             error_msg = data.get("error", "Quota exceeded")
             logger.error(f"ElevenLabs quota exceeded: {error_msg}")
-            await self.push_error(error_msg=f"Quota exceeded: {error_msg}")
+            await self.push_error(ErrorFrame(f"Quota exceeded: {error_msg}"))
 
         else:
             logger.debug(f"Unknown message type: {message_type}")

src/pipecat/services/elevenlabs/tts.py

@@ -160,7 +160,7 @@ def build_elevenlabs_voice_settings(
 class PronunciationDictionaryLocator(BaseModel):
     """Locator for a pronunciation dictionary.
 
-    Parameters:
+    Attributes:
         pronunciation_dictionary_id: The ID of the pronunciation dictionary.
         version_id: The version ID of the pronunciation dictionary.
     """
@@ -424,7 +424,8 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                         json.dumps({"context_id": self._context_id, "close_context": True})
                     )
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._context_id = None
             self._started = False
 
@@ -535,8 +536,9 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
+            logger.error(f"{self} exception: {e}")
             self._websocket = None
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             await self._call_event_handler("on_connection_error", f"{e}")
 
     async def _disconnect_websocket(self):
@@ -551,7 +553,8 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                 await self._websocket.close()
                 logger.debug("Disconnected from ElevenLabs")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._started = False
             self._context_id = None
@@ -581,7 +584,8 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                     json.dumps({"context_id": self._context_id, "close_context": True})
                 )
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._context_id = None
             self._started = False
             self._partial_word = ""
@@ -731,16 +735,20 @@ class ElevenLabsTTSService(AudioContextWordTTSService):
                     await self._websocket.send(json.dumps(msg))
                     logger.trace(f"Created new context {self._context_id}")
 
-                await self._send_text(text)
-                await self.start_tts_usage_metrics(text)
+                    await self._send_text(text)
+                    await self.start_tts_usage_metrics(text)
+                else:
+                    await self._send_text(text)
             except Exception as e:
+                logger.error(f"{self} exception: {e}")
                 yield TTSStoppedFrame()
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 self._started = False
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class ElevenLabsHttpTTSService(WordTTSService):
@@ -1035,6 +1043,7 @@ class ElevenLabsHttpTTSService(WordTTSService):
             ) as response:
                 if response.status != 200:
                     error_text = await response.text()
+                    logger.error(f"{self} error: {error_text}")
                     yield ErrorFrame(error=f"ElevenLabs API error: {error_text}")
                     return
 
@@ -1082,7 +1091,8 @@ class ElevenLabsHttpTTSService(WordTTSService):
                         logger.warning(f"Failed to parse JSON from stream: {e}")
                         continue
                     except Exception as e:
-                        yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                        logger.error(f"{self} exception: {e}")
+                        yield ErrorFrame(error=f"{self} error: {e}")
                         continue
 
                 # After processing all chunks, emit any remaining partial word
@@ -1106,7 +1116,8 @@ class ElevenLabsHttpTTSService(WordTTSService):
                     self._previous_text = text
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
         finally:
             await self.stop_ttfb_metrics()
             # Let the parent class handle TTSStoppedFrame

src/pipecat/services/fal/image.py

@@ -110,6 +110,7 @@ class FalImageGenService(ImageGenService):
         image_url = response["images"][0]["url"] if response else None
 
         if not image_url:
+            logger.error(f"{self} error: image generation failed")
             yield ErrorFrame("Image generation failed")
             return
 

src/pipecat/services/fal/stt.py

@@ -290,4 +290,5 @@ class FalSTTService(SegmentedSTTService):
                     )
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/fish/tts.py

@@ -76,7 +76,7 @@ class FishAudioTTSService(InterruptibleTTSService):
         api_key: str,
         reference_id: Optional[str] = None,  # This is the voice ID
         model: Optional[str] = None,  # Deprecated
-        model_id: str = "s1",
+        model_id: str = "speech-1.5",
         output_format: FishAudioOutputFormat = "pcm",
         sample_rate: Optional[int] = None,
         params: Optional[InputParams] = None,
@@ -93,7 +93,7 @@ class FishAudioTTSService(InterruptibleTTSService):
                 The `model` parameter is deprecated and will be removed in version 0.1.0.
                 Use `reference_id` instead to specify the voice model.
 
-            model_id: Specify which Fish Audio TTS model to use (e.g. "s1")
+            model_id: Specify which Fish Audio TTS model to use (e.g. "speech-1.5")
             output_format: Audio output format. Defaults to "pcm".
             sample_rate: Audio sample rate. If None, uses default.
             params: Additional input parameters for voice customization.
@@ -228,7 +228,8 @@ class FishAudioTTSService(InterruptibleTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -242,7 +243,8 @@ class FishAudioTTSService(InterruptibleTTSService):
                 await self._websocket.send(ormsgpack.packb(stop_message))
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._request_id = None
             self._started = False
@@ -284,7 +286,8 @@ class FishAudioTTSService(InterruptibleTTSService):
                                 continue
 
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     @traced_tts
     async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
@@ -320,7 +323,8 @@ class FishAudioTTSService(InterruptibleTTSService):
                 flush_message = {"event": "flush"}
                 await self._get_websocket().send(ormsgpack.packb(flush_message))
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
@@ -328,4 +332,5 @@ class FishAudioTTSService(InterruptibleTTSService):
             yield None
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/gladia/stt.py

@@ -468,7 +468,8 @@ class GladiaSTTService(STTService):
                             break
 
             except Exception as e:
-                await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                logger.error(f"{self} exception: {e}")
+                await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
                 self._connection_active = False
 
                 if not self._should_reconnect:
@@ -558,7 +559,8 @@ class GladiaSTTService(STTService):
         except websockets.exceptions.ConnectionClosed:
             logger.debug("Connection closed during keepalive")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def _receive_task_handler(self):
         try:
@@ -621,7 +623,8 @@ class GladiaSTTService(STTService):
             # Expected when closing the connection
             pass
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def _maybe_reconnect(self) -> bool:
         """Handle exponential backoff reconnection logic."""
@@ -629,9 +632,7 @@ class GladiaSTTService(STTService):
             return False
         self._reconnection_attempts += 1
         if self._reconnection_attempts > self._max_reconnection_attempts:
-            await self.push_error(
-                error_msg=f"Max reconnection attempts ({self._max_reconnection_attempts}) reached",
-            )
+            logger.error(f"Max reconnection attempts ({self._max_reconnection_attempts}) reached")
             self._should_reconnect = False
             return False
         delay = self._reconnection_delay * (2 ** (self._reconnection_attempts - 1))

src/pipecat/services/google/gemini_live/llm.py

@@ -1175,7 +1175,7 @@ class GeminiLiveLLMService(LLMService):
             self._connection_task = self.create_task(self._connection_task_handler(config=config))
 
         except Exception as e:
-            await self.push_error(error_msg=f"Initialization error: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"{self} Initialization error: {e}"))
 
     async def _connection_task_handler(self, config: LiveConnectConfig):
         async with self._client.aio.live.connect(model=self._model_name, config=config) as session:
@@ -1252,11 +1252,11 @@ class GeminiLiveLLMService(LLMService):
         )
 
         if self._consecutive_failures >= MAX_CONSECUTIVE_FAILURES:
-            error_msg = (
+            logger.error(
                 f"Max consecutive failures ({MAX_CONSECUTIVE_FAILURES}) reached, "
                 "treating as fatal error"
             )
-            await self.push_error(error_msg=error_msg, exception=error)
+            await self.push_error(ErrorFrame(error=f"{self} Error in receive loop: {error}"))
             return False
         else:
             logger.info(
@@ -1284,7 +1284,7 @@ class GeminiLiveLLMService(LLMService):
             self._completed_tool_calls = set()
             self._disconnecting = False
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} error disconnecting: {e}")
 
     async def _send_user_audio(self, frame):
         """Send user audio frame to Gemini Live API."""
@@ -1723,8 +1723,6 @@ class GeminiLiveLLMService(LLMService):
             prompt_tokens=prompt_tokens,
             completion_tokens=completion_tokens,
             total_tokens=total_tokens,
-            cache_read_input_tokens=usage.cached_content_token_count,
-            reasoning_tokens=usage.thoughts_token_count,
         )
 
         await self.start_llm_usage_metrics(tokens)
@@ -1745,7 +1743,7 @@ class GeminiLiveLLMService(LLMService):
         # state management, and that exponential backoff for retries can have
         # cost/stability implications for a service cluster, let's just treat a
         # send-side error as fatal.
-        await self.push_error(error_msg=f"Send error: {error}")
+        await self.push_error(ErrorFrame(error=f"{self} Send error: {error}", fatal=True))
 
     def create_context_aggregator(
         self,

src/pipecat/services/google/image.py

@@ -110,6 +110,7 @@ class GoogleImageGenService(ImageGenService):
             await self.stop_ttfb_metrics()
 
             if not response or not response.generated_images:
+                logger.error(f"{self} error: image generation failed")
                 yield ErrorFrame("Image generation failed")
                 return
 
@@ -127,4 +128,5 @@ class GoogleImageGenService(ImageGenService):
                 yield frame
 
         except Exception as e:
+            logger.error(f"{self} error generating image: {e}")
             yield ErrorFrame(f"Image generation error: {str(e)}")

src/pipecat/services/google/llm.py

@@ -793,7 +793,7 @@ class GoogleLLMService(LLMService):
                 return
             generation_params.setdefault("thinking_config", {})["thinking_budget"] = 0
         except Exception as e:
-            logger.error(f"Failed to unset thinking budget: {e}")
+            logger.exception(f"Failed to unset thinking budget: {e}")
 
     async def _stream_content(
         self, params_from_context: GeminiLLMInvocationParams
@@ -983,7 +983,7 @@ class GoogleLLMService(LLMService):
         except DeadlineExceeded:
             await self._call_event_handler("on_completion_timeout")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.exception(f"{self} exception: {e}")
         finally:
             if grounding_metadata and isinstance(grounding_metadata, dict):
                 llm_search_frame = LLMSearchResponseFrame(

src/pipecat/services/google/stt.py

@@ -774,7 +774,8 @@ class GoogleSTTService(STTService):
                 yield cloud_speech.StreamingRecognizeRequest(audio=audio_data)
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             raise
 
     async def _stream_audio(self):
@@ -805,13 +806,15 @@ class GoogleSTTService(STTService):
                         break
 
                 except Exception as e:
-                    await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+                    logger.error(f"{self} exception: {e}")
+                    await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
                     await asyncio.sleep(1)  # Brief delay before reconnecting
                     self._stream_start_time = int(time.time() * 1000)
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
 
     async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
         """Process an audio chunk for STT transcription.
@@ -899,7 +902,8 @@ class GoogleSTTService(STTService):
             )
             raise
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             # Re-raise the exception to let it propagate (e.g. in the case of a
             # timeout, propagate to _stream_audio to reconnect)
             raise

src/pipecat/services/google/tts.py

@@ -737,6 +737,7 @@ class GoogleHttpTTSService(TTSService):
             yield TTSStoppedFrame()
 
         except Exception as e:
+            logger.error(f"{self} exception: {e}")
             error_message = f"TTS generation error: {str(e)}"
             yield ErrorFrame(error=error_message)
 
@@ -995,7 +996,9 @@ class GoogleTTSService(GoogleBaseTTSService):
                 yield frame
 
         except Exception as e:
-            await self.push_error(error_msg=f"TTS generation error: {str(e)}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            error_message = f"TTS generation error: {str(e)}"
+            yield ErrorFrame(error=error_message)
 
 
 class GeminiTTSService(GoogleBaseTTSService):
@@ -1245,5 +1248,6 @@ class GeminiTTSService(GoogleBaseTTSService):
                 yield frame
 
         except Exception as e:
+            logger.error(f"{self} exception: {e}")
             error_message = f"Gemini TTS generation error: {str(e)}"
             yield ErrorFrame(error=error_message)

src/pipecat/services/gradium/__init__.py

@@ -1,5 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#

src/pipecat/services/gradium/stt.py

@@ -1,239 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""Gradium's speech-to-text service implementation.
-
-This module provides integration with Gradium's real-time speech-to-text
-WebSocket API for streaming audio transcription.
-"""
-
-import base64
-import json
-from typing import AsyncGenerator
-
-from loguru import logger
-
-from pipecat.frames.frames import (
-    CancelFrame,
-    EndFrame,
-    Frame,
-    StartFrame,
-    TranscriptionFrame,
-)
-from pipecat.services.stt_service import WebsocketSTTService
-from pipecat.transcriptions.language import Language
-from pipecat.utils.time import time_now_iso8601
-from pipecat.utils.tracing.service_decorators import traced_stt
-
-try:
-    from websockets.asyncio.client import connect as websocket_connect
-    from websockets.protocol import State
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error('In order to use Gradium, you need to `pip install "pipecat-ai[gradium]"`.')
-    raise Exception(f"Missing module: {e}")
-
-SAMPLE_RATE = 24000
-
-
-class GradiumSTTService(WebsocketSTTService):
-    """Gradium real-time speech-to-text service.
-
-    Provides real-time speech transcription using Gradium's WebSocket API.
-    Supports both interim and final transcriptions with configurable parameters
-    for audio processing and connection management.
-    """
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        api_endpoint_base_url: str = "wss://eu.api.gradium.ai/api/speech/asr",
-        json_config: str | None = None,
-        **kwargs,
-    ):
-        """Initialize the Gradium STT service.
-
-        Args:
-            api_key: Gradium API key for authentication.
-            api_endpoint_base_url: WebSocket endpoint URL. Defaults to Gradium's streaming endpoint.
-            json_config: Optional JSON configuration string for additional model settings.
-            **kwargs: Additional arguments passed to parent STTService class.
-        """
-        super().__init__(sample_rate=SAMPLE_RATE, **kwargs)
-
-        self._api_key = api_key
-        self._api_endpoint_base_url = api_endpoint_base_url
-        self._websocket = None
-        self._json_config = json_config
-
-        self._receive_task = None
-
-        self._audio_buffer = bytearray()
-        self._chunk_size_ms = 80
-        self._chunk_size_bytes = 0
-
-    def can_generate_metrics(self) -> bool:
-        """Check if the service can generate metrics.
-
-        Returns:
-            True if metrics generation is supported.
-        """
-        return True
-
-    async def start(self, frame: StartFrame):
-        """Start the speech-to-text service.
-
-        Args:
-            frame: Start frame to begin processing.
-        """
-        await super().start(frame)
-        self._chunk_size_bytes = int(self._chunk_size_ms * self.sample_rate * 2 / 1000)
-        await self._connect()
-
-    async def stop(self, frame: EndFrame):
-        """Stop the speech-to-text service.
-
-        Args:
-            frame: End frame to stop processing.
-        """
-        await super().stop(frame)
-        await self._disconnect()
-
-    async def cancel(self, frame: CancelFrame):
-        """Cancel the speech-to-text service.
-
-        Args:
-            frame: Cancel frame to abort processing.
-        """
-        await super().cancel(frame)
-        await self._disconnect()
-
-    async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
-        """Process audio data for speech-to-text conversion.
-
-        Args:
-            audio: Raw audio bytes to process.
-
-        Yields:
-            None (processing handled via WebSocket messages).
-        """
-        self._audio_buffer.extend(audio)
-        await self.start_ttfb_metrics()
-        await self.start_processing_metrics()
-
-        while len(self._audio_buffer) >= self._chunk_size_bytes:
-            chunk = bytes(self._audio_buffer[: self._chunk_size_bytes])
-            self._audio_buffer = self._audio_buffer[self._chunk_size_bytes :]
-            chunk = base64.b64encode(chunk).decode("utf-8")
-            msg = {"type": "audio", "audio": chunk}
-            if self._websocket and self._websocket.state is State.OPEN:
-                await self._websocket.send(json.dumps(msg))
-
-        yield None
-
-    @traced_stt
-    async def _trace_transcription(self, transcript: str, is_final: bool, language: Language):
-        """Record transcription event for tracing."""
-        pass
-
-    async def _connect(self):
-        await self._connect_websocket()
-
-        if self._websocket and not self._receive_task:
-            self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
-
-    async def _connect_websocket(self):
-        try:
-            if self._websocket and self._websocket.state is State.OPEN:
-                return
-            ws_url = self._api_endpoint_base_url
-            headers = {
-                "x-api-key": self._api_key,
-                "x-api-source": "pipecat",
-            }
-            self._websocket = await websocket_connect(
-                ws_url,
-                additional_headers=headers,
-            )
-            await self._call_event_handler("on_connected")
-            setup_msg = {
-                "type": "setup",
-                "input_format": "pcm",
-            }
-            if self._json_config is not None:
-                setup_msg["json_config"] = self._json_config
-            await self._websocket.send(json.dumps(setup_msg))
-            ready_msg = await self._websocket.recv()
-            ready_msg = json.loads(ready_msg)
-            if ready_msg["type"] == "error":
-                raise Exception(f"received error {ready_msg['message']}")
-            if ready_msg["type"] != "ready":
-                raise Exception(f"unexpected first message type {ready_msg['type']}")
-
-        except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
-            raise
-
-    async def _disconnect(self):
-        if self._receive_task:
-            await self.cancel_task(self._receive_task)
-            self._receive_task = None
-
-        await self._disconnect_websocket()
-
-    async def _disconnect_websocket(self):
-        try:
-            if self._websocket and self._websocket.state is State.OPEN:
-                logger.debug("Disconnecting from Gradium STT")
-                await self._websocket.close()
-        except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
-        finally:
-            self._websocket = None
-            await self._call_event_handler("on_disconnected")
-
-    def _get_websocket(self):
-        if self._websocket:
-            return self._websocket
-        raise Exception("Websocket not connected")
-
-    async def _process_messages(self):
-        async for message in self._get_websocket():
-            try:
-                data = json.loads(message)
-                await self._process_response(data)
-            except json.JSONDecodeError:
-                logger.warning(f"Received non-JSON message: {message}")
-
-    async def _receive_messages(self):
-        while True:
-            await self._process_messages()
-            logger.debug(f"{self} Gradium connection was disconnected (timeout?), reconnecting")
-            await self._connect_websocket()
-
-    async def _process_response(self, msg):
-        type_ = msg.get("type", "")
-        if type_ == "text":
-            await self._handle_text(msg["text"])
-        elif type_ == "end_of_stream":
-            await self._handle_end_of_stream()
-        elif type_ == "error":
-            await self.push_error(error_msg=f"Error: {msg}")
-
-    async def _handle_end_of_stream(self):
-        """Handle termination message."""
-        logger.debug("Received end_of_stream message from server")
-
-    async def _handle_text(self, text: str):
-        """Handle transcription results."""
-        await self.push_frame(
-            TranscriptionFrame(
-                text,
-                self._user_id,
-                time_now_iso8601(),
-            )
-        )

src/pipecat/services/gradium/tts.py

@@ -1,315 +0,0 @@
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-
-"""Gradium Text-to-Speech service implementation."""
-
-import base64
-import json
-import uuid
-from typing import Any, AsyncGenerator, Mapping, Optional
-
-from loguru import logger
-from pydantic import BaseModel
-
-from pipecat.frames.frames import (
-    CancelFrame,
-    EndFrame,
-    ErrorFrame,
-    Frame,
-    StartFrame,
-    TTSAudioRawFrame,
-    TTSStartedFrame,
-    TTSStoppedFrame,
-)
-from pipecat.processors.frame_processor import FrameDirection
-from pipecat.services.tts_service import InterruptibleWordTTSService
-from pipecat.utils.tracing.service_decorators import traced_tts
-
-try:
-    from websockets import ConnectionClosedOK
-    from websockets.asyncio.client import connect as websocket_connect
-    from websockets.protocol import State
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error("In order to use Gradium, you need to `pip install pipecat-ai[gradium]`.")
-    raise Exception(f"Missing module: {e}")
-
-SAMPLE_RATE = 48000
-
-
-class GradiumTTSService(InterruptibleWordTTSService):
-    """Text-to-Speech service using Gradium's websocket API."""
-
-    class InputParams(BaseModel):
-        """Configuration parameters for Gradium TTS service.
-
-        Parameters:
-            temp: Temperature to be used for generation, defaults to 0.6.
-        """
-
-        temp: Optional[float] = 0.6
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        voice_id: str = "YTpq7expH9539ERJ",
-        url: str = "wss://eu.api.gradium.ai/api/speech/tts",
-        model: str = "default",
-        json_config: Optional[str] = None,
-        params: Optional[InputParams] = None,
-        **kwargs,
-    ):
-        """Initialize the Gradium TTS service.
-
-        Args:
-            api_key: Gradium API key for authentication.
-            voice_id: the voice identifier.
-            url: Gradium websocket API endpoint.
-            model: Model ID to use for synthesis.
-            json_config: Optional JSON configuration string for additional model settings.
-            params: Additional configuration parameters.
-            **kwargs: Additional arguments passed to parent class.
-        """
-        # Initialize with parent class settings for proper frame handling
-        super().__init__(
-            push_stop_frames=True,
-            pause_frame_processing=True,
-            sample_rate=SAMPLE_RATE,
-            **kwargs,
-        )
-
-        params = params or GradiumTTSService.InputParams()
-
-        # Store service configuration
-        self._api_key = api_key
-        self._url = url
-        self._voice_id = voice_id
-        self._json_config = json_config
-        self._model = model
-        self._settings = {
-            "voice_id": voice_id,
-            "model_name": model,
-            "output_format": "pcm",
-        }
-
-        # State tracking
-        self._receive_task = None
-
-    def can_generate_metrics(self) -> bool:
-        """Check if this service can generate processing metrics.
-
-        Returns:
-            True, as Gradium service supports metrics generation.
-        """
-        return True
-
-    async def set_model(self, model: str):
-        """Update the TTS model.
-
-        Args:
-            model: The model name to use for synthesis.
-        """
-        self._model = model
-        await super().set_model(model)
-
-    async def _update_settings(self, settings: Mapping[str, Any]):
-        """Update service settings and reconnect if voice changed."""
-        prev_voice = self._voice_id
-        await super()._update_settings(settings)
-        if not prev_voice == self._voice_id:
-            self._settings["voice_id"] = self._voice_id
-            logger.info(f"Switching TTS voice to: [{self._voice_id}]")
-            await self._disconnect()
-            await self._connect()
-
-    def _build_msg(self, text: str = "") -> dict:
-        """Build JSON message for Gradium API."""
-        return {"text": text, "type": "text"}
-
-    async def start(self, frame: StartFrame):
-        """Start the service and establish websocket connection.
-
-        Args:
-            frame: The start frame containing initialization parameters.
-        """
-        await super().start(frame)
-        await self._connect()
-
-    async def stop(self, frame: EndFrame):
-        """Stop the service and close connection.
-
-        Args:
-            frame: The end frame.
-        """
-        await super().stop(frame)
-        await self._disconnect()
-
-    async def cancel(self, frame: CancelFrame):
-        """Cancel current operation and clean up.
-
-        Args:
-            frame: The cancel frame.
-        """
-        await super().cancel(frame)
-        await self._disconnect()
-
-    async def _connect(self):
-        """Establish websocket connection and start receive task."""
-        logger.debug(f"{self}: connecting")
-
-        # If the server disconnected, cancel the receive-task so that it can be reset below.
-        if self._websocket is None or self._websocket.state is not State.OPEN:
-            if self._receive_task:
-                await self.cancel_task(self._receive_task)
-                self._receive_task = None
-
-        await self._connect_websocket()
-
-        if self._websocket and not self._receive_task:
-            logger.debug(f"{self}: setting receive task")
-            self._receive_task = self.create_task(self._receive_task_handler(self._report_error))
-
-    async def _disconnect(self):
-        """Close websocket connection and clean up tasks."""
-        logger.debug(f"{self}: disconnecting")
-        if self._receive_task:
-            await self.cancel_task(self._receive_task)
-            self._receive_task = None
-
-        await self._disconnect_websocket()
-
-    async def _connect_websocket(self):
-        """Connect to Gradium websocket API with configured settings."""
-        try:
-            if self._websocket and self._websocket.state is State.OPEN:
-                return
-
-            headers = {"x-api-key": self._api_key, "x-api-source": "pipecat"}
-            self._websocket = await websocket_connect(self._url, additional_headers=headers)
-
-            setup_msg = {
-                "type": "setup",
-                "output_format": "pcm",
-                "voice_id": self._voice_id,
-            }
-            if self._json_config is not None:
-                setup_msg["json_config"] = self._json_config
-            await self._websocket.send(json.dumps(setup_msg))
-            ready_msg = await self._websocket.recv()
-            ready_msg = json.loads(ready_msg)
-            if ready_msg["type"] == "error":
-                raise Exception(f"received error {ready_msg['message']}")
-            if ready_msg["type"] != "ready":
-                raise Exception(f"unexpected first message type {ready_msg['type']}")
-
-            await self._call_event_handler("on_connected")
-        except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
-            self._websocket = None
-            await self._call_event_handler("on_connection_error", f"{e}")
-
-    async def _disconnect_websocket(self):
-        """Close websocket connection and reset state."""
-        try:
-            await self.stop_all_metrics()
-            if self._websocket:
-                await self._websocket.close()
-        except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
-        finally:
-            self._websocket = None
-            await self._call_event_handler("on_disconnected")
-
-    def _get_websocket(self):
-        """Get active websocket connection or raise exception."""
-        if self._websocket:
-            return self._websocket
-        raise Exception("Websocket not connected")
-
-    async def flush_audio(self):
-        """Flush any pending audio synthesis."""
-        if not self._websocket:
-            return
-        try:
-            msg = {"type": "end_of_stream"}
-            await self._websocket.send(json.dumps(msg))
-        except ConnectionClosedOK:
-            logger.debug(f"{self}: connection closed normally during flush")
-        except Exception as e:
-            logger.error(f"{self} exception: {e}")
-
-    async def _receive_messages(self):
-        """Process incoming websocket messages."""
-        # TODO(laurent): This should not be necessary as it should happen when
-        # receiving the messages but this does not seem to always be the case
-        # and that may lead to a busy polling loop.
-        if self._websocket and self._websocket.state is State.CLOSED:
-            raise ConnectionClosedOK(None, None)
-        async for message in self._get_websocket():
-            msg = json.loads(message)
-
-            if msg["type"] == "audio":
-                # Process audio chunk
-                await self.stop_ttfb_metrics()
-                self.start_word_timestamps()
-                frame = TTSAudioRawFrame(
-                    audio=base64.b64decode(msg["audio"]),
-                    sample_rate=self.sample_rate,
-                    num_channels=1,
-                )
-                await self.push_frame(frame)
-
-            elif msg["type"] == "text":
-                await self.add_word_timestamps([(msg["text"], msg["start_s"])])
-            elif msg["type"] == "end_of_stream":
-                await self.push_frame(TTSStoppedFrame())
-                await self.stop_all_metrics()
-
-            elif msg["type"] == "error":
-                await self.push_frame(TTSStoppedFrame())
-                await self.stop_all_metrics()
-                await self.push_error(error_msg=f"Error: {msg['message']}")
-
-    async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
-        """Push frame and handle end-of-turn conditions.
-
-        Args:
-            frame: The frame to push.
-            direction: The direction to push the frame.
-        """
-        await super().push_frame(frame, direction)
-
-    @traced_tts
-    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        """Generate speech from text using Gradium's streaming API.
-
-        Args:
-            text: The text to convert to speech.
-
-        Yields:
-            Frame: Audio frames containing the synthesized speech.
-        """
-        _state = self._websocket.state if self._websocket is not None else None
-        logger.debug(f"{self}: Generating TTS [{text}] {_state}")
-        try:
-            if not self._websocket or self._websocket.state is State.CLOSED:
-                self._websocket = None
-                await self._connect()
-
-            try:
-                yield TTSStartedFrame()
-
-                msg = self._build_msg(text=text)
-                await self._get_websocket().send(json.dumps(msg))
-                await self.start_tts_usage_metrics(text)
-            except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
-                yield TTSStoppedFrame()
-                await self._disconnect()
-                await self._connect()
-                return
-            yield None
-        except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")

src/pipecat/services/grok/llm.py

@@ -123,8 +123,6 @@ class GrokLLMService(OpenAILLMService):
         self._prompt_tokens = 0
         self._completion_tokens = 0
         self._total_tokens = 0
-        self._cache_read_input_tokens = None
-        self._reasoning_tokens = None
         self._has_reported_prompt_tokens = False
         self._is_processing = True
 
@@ -139,8 +137,6 @@ class GrokLLMService(OpenAILLMService):
                     prompt_tokens=self._prompt_tokens,
                     completion_tokens=self._completion_tokens,
                     total_tokens=self._total_tokens,
-                    cache_read_input_tokens=self._cache_read_input_tokens,
-                    reasoning_tokens=self._reasoning_tokens,
                 )
                 await super().start_llm_usage_metrics(tokens)
 
@@ -153,7 +149,7 @@ class GrokLLMService(OpenAILLMService):
 
         Args:
             tokens: The token usage metrics for the current chunk of processing,
-                containing prompt_tokens, completion_tokens, and optional cached/reasoning tokens.
+                containing prompt_tokens and completion_tokens counts.
         """
         # Only accumulate metrics during active processing
         if not self._is_processing:
@@ -168,13 +164,6 @@ class GrokLLMService(OpenAILLMService):
         if tokens.completion_tokens > self._completion_tokens:
             self._completion_tokens = tokens.completion_tokens
 
-        # Capture cached & reasoning tokens (these typically only appear once per request)
-        if tokens.cache_read_input_tokens is not None:
-            self._cache_read_input_tokens = tokens.cache_read_input_tokens
-
-        if tokens.reasoning_tokens is not None:
-            self._reasoning_tokens = tokens.reasoning_tokens
-
     def create_context_aggregator(
         self,
         context: OpenAILLMContext,

src/pipecat/services/groq/tts.py

@@ -146,6 +146,7 @@ class GroqTTSService(TTSService):
                     bytes = w.readframes(num_frames)
                     yield TTSAudioRawFrame(bytes, frame_rate, channels)
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
         yield TTSStoppedFrame()

src/pipecat/services/heygen/client.py

@@ -179,7 +179,7 @@ class HeyGenClient:
                 await self._task_manager.cancel_task(self._event_task)
                 self._event_task = None
         except Exception as e:
-            logger.error(f"Exception during cleanup: {e}")
+            logger.exception(f"Exception during cleanup: {e}")
 
     async def start(self, frame: StartFrame, audio_chunk_size: int) -> None:
         """Start the client and establish all necessary connections.

src/pipecat/services/hume/tts.py

@@ -8,14 +8,10 @@ import base64
 import os
 from typing import Any, AsyncGenerator, Optional
 
-import httpx
 from loguru import logger
 from pydantic import BaseModel
 
-from pipecat import __version__
 from pipecat.frames.frames import (
-    CancelFrame,
-    EndFrame,
     ErrorFrame,
     Frame,
     InterruptionFrame,
@@ -30,7 +26,11 @@ from pipecat.utils.tracing.service_decorators import traced_tts
 
 try:
     from hume import AsyncHumeClient
-    from hume.tts import FormatPcm, PostedUtterance, PostedUtteranceVoiceWithId
+    from hume.tts import (
+        FormatPcm,
+        PostedUtterance,
+        PostedUtteranceVoiceWithId,
+    )
     from hume.tts.types import TimestampMessage
 except ModuleNotFoundError as e:  # pragma: no cover - import-time guidance
     logger.error(f"Exception: {e}")
@@ -40,12 +40,6 @@ except ModuleNotFoundError as e:  # pragma: no cover - import-time guidance
 
 HUME_SAMPLE_RATE = 48_000  # Hume TTS streams at 48 kHz
 
-# Tracking headers for Hume API requests
-DEFAULT_HEADERS = {
-    "X-Hume-Client-Name": "pipecat",
-    "X-Hume-Client-Version": __version__,
-}
-
 
 class HumeTTSService(WordTTSService):
     """Hume Octave Text-to-Speech service.
@@ -110,11 +104,7 @@ class HumeTTSService(WordTTSService):
             **kwargs,
         )
 
-        # Create a custom httpx.AsyncClient with tracking headers
-        # Headers are included in all requests made by the Hume SDK
-        self._http_client = httpx.AsyncClient(headers=DEFAULT_HEADERS)
-
-        self._client = AsyncHumeClient(api_key=api_key, httpx_client=self._http_client)
+        self._client = AsyncHumeClient(api_key=api_key)
         self._params = params or HumeTTSService.InputParams()
 
         # Store voice in the base class (mirrors other services)
@@ -148,26 +138,6 @@ class HumeTTSService(WordTTSService):
         self._cumulative_time = 0.0
         self._started = False
 
-    async def stop(self, frame: EndFrame) -> None:
-        """Stop the service and cleanup resources.
-
-        Args:
-            frame: The end frame.
-        """
-        await super().stop(frame)
-        if hasattr(self, "_http_client") and self._http_client:
-            await self._http_client.aclose()
-
-    async def cancel(self, frame: CancelFrame) -> None:
-        """Cancel the service and cleanup resources.
-
-        Args:
-            frame: The cancel frame.
-        """
-        await super().cancel(frame)
-        if hasattr(self, "_http_client") and self._http_client:
-            await self._http_client.aclose()
-
     async def push_frame(self, frame: Frame, direction: FrameDirection = FrameDirection.DOWNSTREAM):
         """Push a frame and handle state changes.
 
@@ -317,7 +287,8 @@ class HumeTTSService(WordTTSService):
                 self._cumulative_time = utterance_duration
 
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             # Ensure TTFB timer is stopped even on early failures
             await self.stop_ttfb_metrics()

src/pipecat/services/inworld/tts.py

@@ -397,7 +397,8 @@ class InworldTTSService(TTSService):
             # STEP 7: ERROR HANDLING
             # ================================================================================
             # Log any unexpected errors and notify the pipeline
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             # ================================================================================
             # STEP 8: CLEANUP AND COMPLETION
@@ -512,7 +513,7 @@ class InworldTTSService(TTSService):
         # Extract the base64-encoded audio content from response
         if "audioContent" not in response_data:
             logger.error("No audioContent in Inworld API response")
-            yield ErrorFrame(error="No audioContent in response")
+            await self.push_error(ErrorFrame(error="No audioContent in response"))
             return
 
         # ================================================================================

src/pipecat/services/llm_service.py

@@ -173,17 +173,16 @@ class LLMService(AIService):
             run_in_parallel: Whether to run function calls in parallel or sequentially.
                 Defaults to True.
             **kwargs: Additional arguments passed to the parent AIService.
-
         """
         super().__init__(**kwargs)
         self._run_in_parallel = run_in_parallel
         self._start_callbacks = {}
         self._adapter = self.adapter_class()
         self._functions: Dict[Optional[str], FunctionCallRegistryItem] = {}
-        self._function_call_tasks: Dict[Optional[asyncio.Task], FunctionCallRunnerItem] = {}
+        self._function_call_tasks: Dict[asyncio.Task, FunctionCallRunnerItem] = {}
         self._sequential_runner_task: Optional[asyncio.Task] = None
         self._tracing_enabled: bool = False
-        self._skip_tts: Optional[bool] = None
+        self._skip_tts: bool = False
 
         self._register_event_handler("on_function_calls_started")
         self._register_event_handler("on_completion_timeout")
@@ -294,8 +293,7 @@ class LLMService(AIService):
             direction: The direction of frame pushing.
         """
         if isinstance(frame, (LLMTextFrame, LLMFullResponseStartFrame, LLMFullResponseEndFrame)):
-            if self._skip_tts is not None:
-                frame.skip_tts = self._skip_tts
+            frame.skip_tts = self._skip_tts
 
         await super().push_frame(frame, direction)
 
@@ -437,7 +435,6 @@ class LLMService(AIService):
 
         await self.broadcast_frame(FunctionCallsStartedFrame, function_calls=function_calls)
 
-        runner_items = []
         for function_call in function_calls:
             if function_call.function_name in self._functions.keys():
                 item = self._functions[function_call.function_name]
@@ -449,20 +446,28 @@ class LLMService(AIService):
                 )
                 continue
 
-            runner_items.append(
-                FunctionCallRunnerItem(
-                    registry_item=item,
-                    function_name=function_call.function_name,
-                    tool_call_id=function_call.tool_call_id,
-                    arguments=function_call.arguments,
-                    context=function_call.context,
-                )
+            runner_item = FunctionCallRunnerItem(
+                registry_item=item,
+                function_name=function_call.function_name,
+                tool_call_id=function_call.tool_call_id,
+                arguments=function_call.arguments,
+                context=function_call.context,
             )
 
-        if self._run_in_parallel:
-            await self._run_parallel_function_calls(runner_items)
-        else:
-            await self._run_sequential_function_calls(runner_items)
+            if self._run_in_parallel:
+                task = self.create_task(self._run_function_call(runner_item))
+                self._function_call_tasks[task] = runner_item
+                task.add_done_callback(self._function_call_task_finished)
+            else:
+                await self._sequential_runner_queue.put(runner_item)
+
+    async def _call_start_function(
+        self, context: OpenAILLMContext | LLMContext, function_name: str
+    ):
+        if function_name in self._start_callbacks.keys():
+            await self._start_callbacks[function_name](function_name, self, context)
+        elif None in self._start_callbacks.keys():
+            return await self._start_callbacks[None](function_name, self, context)
 
     async def request_image_frame(
         self,
@@ -535,27 +540,6 @@ class LLMService(AIService):
             await task
             del self._function_call_tasks[task]
 
-    async def _run_parallel_function_calls(self, runner_items: Sequence[FunctionCallRunnerItem]):
-        tasks = []
-        for runner_item in runner_items:
-            task = self.create_task(self._run_function_call(runner_item))
-            tasks.append(task)
-            self._function_call_tasks[task] = runner_item
-            task.add_done_callback(self._function_call_task_finished)
-
-    async def _run_sequential_function_calls(self, runner_items: Sequence[FunctionCallRunnerItem]):
-        # Enqueue all function calls for background execution.
-        for runner_item in runner_items:
-            await self._sequential_runner_queue.put(runner_item)
-
-    async def _call_start_function(
-        self, context: OpenAILLMContext | LLMContext, function_name: str
-    ):
-        if function_name in self._start_callbacks.keys():
-            await self._start_callbacks[function_name](function_name, self, context)
-        elif None in self._start_callbacks.keys():
-            return await self._start_callbacks[None](function_name, self, context)
-
     async def _run_function_call(self, runner_item: FunctionCallRunnerItem):
         if runner_item.function_name in self._functions.keys():
             item = self._functions[runner_item.function_name]
@@ -639,19 +623,20 @@ class LLMService(AIService):
                 name = runner_item.function_name
                 tool_call_id = runner_item.tool_call_id
 
+                # We remove the callback because we are going to cancel the task
+                # now, otherwise we will be removing it from the set while we
+                # are iterating.
+                task.remove_done_callback(self._function_call_task_finished)
+
                 logger.debug(f"{self} Cancelling function call [{name}:{tool_call_id}]...")
 
-                if task:
-                    # We remove the callback because we are going to cancel the
-                    # task next, otherwise we will be removing it from the set
-                    # while we are iterating.
-                    task.remove_done_callback(self._function_call_task_finished)
-                    await self.cancel_task(task)
-                    cancelled_tasks.add(task)
+                await self.cancel_task(task)
 
                 frame = FunctionCallCancelFrame(function_name=name, tool_call_id=tool_call_id)
                 await self.push_frame(frame)
 
+                cancelled_tasks.add(task)
+
                 logger.debug(f"{self} Function call [{name}:{tool_call_id}] has been cancelled")
 
         # Remove all cancelled tasks from our set.

src/pipecat/services/lmnt/tts.py

@@ -214,7 +214,8 @@ class LmntTTSService(InterruptibleTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -230,7 +231,8 @@ class LmntTTSService(InterruptibleTTSService):
                 # await self._websocket.send(json.dumps({"eof": True}))
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting from LMNT: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._started = False
             self._websocket = None
@@ -264,9 +266,10 @@ class LmntTTSService(InterruptibleTTSService):
                 try:
                     msg = json.loads(message)
                     if "error" in msg:
+                        logger.error(f"{self} error: {msg['error']}")
                         await self.push_frame(TTSStoppedFrame())
                         await self.stop_all_metrics()
-                        await self.push_error(error_msg=f"Error: {msg['error']}")
+                        await self.push_error(ErrorFrame(error=f"{self} error: {msg['error']}"))
                         return
                 except json.JSONDecodeError:
                     logger.error(f"Invalid JSON message: {message}")
@@ -299,11 +302,13 @@ class LmntTTSService(InterruptibleTTSService):
                 await self._get_websocket().send(json.dumps({"flush": True}))
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/mcp_service.py

@@ -7,7 +7,7 @@
 """MCP (Model Context Protocol) client for integrating external tools with LLMs."""
 
 import json
-from typing import Any, Callable, Dict, List, Optional, TypeAlias
+from typing import Any, Dict, List, TypeAlias
 
 from loguru import logger
 
@@ -46,24 +46,17 @@ class MCPClient(BaseObject):
     def __init__(
         self,
         server_params: ServerParameters,
-        tools_filter: Optional[List[str]] = None,
-        tools_output_filters: Optional[Dict[str, Callable[[Any], Any]]] = None,
         **kwargs,
     ):
         """Initialize the MCP client with server parameters.
 
         Args:
             server_params: Server connection parameters (stdio or SSE).
-            tools_filter: Optional list of tool names to register. If None, all tools are registered.
-            tools_output_filters: Optional dict mapping tool names to filter functions that process tool outputs.
-                                  Each filter function receives the raw tool output (any type) and returns the processed output (any type).
             **kwargs: Additional arguments passed to the parent BaseObject.
         """
         super().__init__(**kwargs)
         self._server_params = server_params
         self._session = ClientSession
-        self._tools_filter = tools_filter
-        self._tools_output_filters = tools_output_filters or {}
 
         if isinstance(server_params, StdioServerParameters):
             self._client = stdio_client
@@ -183,6 +176,7 @@ class MCPClient(BaseObject):
         except Exception as e:
             error_msg = f"Error calling mcp tool {params.function_name}: {str(e)}"
             logger.error(error_msg)
+            logger.exception("Full exception details:")
             await params.result_callback(error_msg)
 
     async def _stdio_list_tools(self) -> ToolsSchema:
@@ -213,6 +207,7 @@ class MCPClient(BaseObject):
         except Exception as e:
             error_msg = f"Error calling mcp tool {params.function_name}: {str(e)}"
             logger.error(error_msg)
+            logger.exception("Full exception details:")
             await params.result_callback(error_msg)
 
     async def _streamable_http_list_tools(self) -> ToolsSchema:
@@ -251,6 +246,7 @@ class MCPClient(BaseObject):
         except Exception as e:
             error_msg = f"Error calling mcp tool {params.function_name}: {str(e)}"
             logger.error(error_msg)
+            logger.exception("Full exception details:")
             await params.result_callback(error_msg)
 
     async def _call_tool(self, session, function_name, arguments, result_callback):
@@ -271,26 +267,13 @@ class MCPClient(BaseObject):
                     else:
                         # logger.debug(f"Non-text result content: '{content}'")
                         pass
+                logger.info(f"Tool '{function_name}' completed successfully")
+                logger.debug(f"Final response: {response}")
             else:
                 logger.error(f"Error getting content from {function_name} results.")
 
-        # Apply output filter if configured for this tool
-        if function_name in self._tools_output_filters:
-            try:
-                response = self._tools_output_filters[function_name](response)
-                logger.debug(f"Final response (after filter): {response}")
-
-            except Exception:
-                logger.error(f"Error applying output filter for {function_name}")
-                response = ""
-
-        if response and len(response) and isinstance(response, str):
-            logger.info(f"Tool '{function_name}' completed successfully")
-            logger.debug(f"Final response: {response}")
-        else:
-            response = "Sorry, could not call the mcp tool"
-
-        await result_callback(response)
+        final_response = response if len(response) else "Sorry, could not call the mcp tool"
+        await result_callback(final_response)
 
     async def _list_tools_helper(self, session):
         available_tools = await session.list_tools()
@@ -303,12 +286,6 @@ class MCPClient(BaseObject):
 
         for tool in available_tools.tools:
             tool_name = tool.name
-
-            # Apply tools filter if configured
-            if self._tools_filter and tool_name not in self._tools_filter:
-                logger.debug(f"Skipping tool '{tool_name}' - not in allowed tools list")
-                continue
-
             logger.debug(f"Processing tool: {tool_name}")
             logger.debug(f"Tool description: {tool.description}")
 
@@ -325,6 +302,7 @@ class MCPClient(BaseObject):
 
             except Exception as e:
                 logger.error(f"Failed to read tool '{tool_name}': {str(e)}")
+                logger.exception("Full exception details:")
                 continue
 
         logger.debug(f"Completed reading {len(tool_schemas)} tools")

src/pipecat/services/mem0/memory.py

@@ -253,9 +253,8 @@ class Mem0MemoryService(FrameProcessor):
                     # Otherwise, pass the enhanced context frame downstream
                     await self.push_frame(frame)
             except Exception as e:
-                await self.push_error(
-                    error_msg=f"Error processing with Mem0: {str(e)}", exception=e
-                )
+                logger.error(f"Error processing with Mem0: {str(e)}")
+                await self.push_frame(ErrorFrame(f"Error processing with Mem0: {str(e)}"))
                 await self.push_frame(frame)  # Still pass the original frame through
         else:
             # For non-context frames, just pass them through

src/pipecat/services/minimax/tts.py

@@ -314,6 +314,7 @@ class MiniMaxHttpTTSService(TTSService):
             ) as response:
                 if response.status != 200:
                     error_message = f"MiniMax TTS error: HTTP {response.status}"
+                    logger.error(error_message)
                     yield ErrorFrame(error=error_message)
                     return
 
@@ -391,7 +392,8 @@ class MiniMaxHttpTTSService(TTSService):
                             continue
 
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/moondream/vision.py

@@ -110,6 +110,7 @@ class MoondreamService(VisionService):
                   if analysis fails.
         """
         if not self._model:
+            logger.error(f"{self} error: Moondream model not available ({self.model_name})")
             yield ErrorFrame("Moondream model not available")
             return
 

src/pipecat/services/neuphonic/tts.py

@@ -285,7 +285,8 @@ class NeuphonicTTSService(InterruptibleTTSService):
 
             await self._call_event_handler("on_connected")
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
             self._websocket = None
             await self._call_event_handler("on_connection_error", f"{e}")
 
@@ -298,7 +299,8 @@ class NeuphonicTTSService(InterruptibleTTSService):
                 logger.debug("Disconnecting from Neuphonic")
                 await self._websocket.close()
         except Exception as e:
-            await self.push_error(error_msg=f"Unknown error occurred: {e}", exception=e)
+            logger.error(f"{self} exception: {e}")
+            await self.push_error(ErrorFrame(error=f"{self} error: {e}"))
         finally:
             self._started = False
             self._websocket = None
@@ -363,14 +365,16 @@ class NeuphonicTTSService(InterruptibleTTSService):
                 await self._send_text(text)
                 await self.start_tts_usage_metrics(text)
             except Exception as e:
-                yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                logger.error(f"{self} exception: {e}")
+                yield ErrorFrame(error=f"{self} error: {e}")
                 yield TTSStoppedFrame()
                 await self._disconnect()
                 await self._connect()
                 return
             yield None
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
 
 
 class NeuphonicHttpTTSService(TTSService):
@@ -534,6 +538,7 @@ class NeuphonicHttpTTSService(TTSService):
                 if response.status != 200:
                     error_text = await response.text()
                     error_message = f"Neuphonic API error: HTTP {response.status} - {error_text}"
+                    logger.error(error_message)
                     yield ErrorFrame(error=error_message)
                     return
 
@@ -563,7 +568,8 @@ class NeuphonicHttpTTSService(TTSService):
                             yield TTSAudioRawFrame(audio_bytes, self.sample_rate, 1)
 
                     except Exception as e:
-                        yield ErrorFrame(error=f"Unknown error occurred: {e}")
+                        logger.error(f"{self} exception: {e}")
+                        yield ErrorFrame(error=f"{self} error: {e}")
                         # Don't yield error frame for individual message failures
                         continue
 
@@ -571,7 +577,8 @@ class NeuphonicHttpTTSService(TTSService):
             logger.debug("TTS generation cancelled")
             raise
         except Exception as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.error(f"{self} exception: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")
         finally:
             await self.stop_ttfb_metrics()
             yield TTSStoppedFrame()

src/pipecat/services/nim/llm.py

@@ -8,23 +8,98 @@
 
 This module provides a service for interacting with NVIDIA's NIM (NVIDIA Inference
 Microservice) API while maintaining compatibility with the OpenAI-style interface.
-
-.. deprecated:: 0.0.96
-    This module is deprecated. Please NvidiaLLMService from
-    pipecat.services.nvidia.llm instead.
 """
 
-import warnings
+from pipecat.metrics.metrics import LLMTokenUsage
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.openai.llm import OpenAILLMService
 
-from pipecat.services.nvidia.llm import NvidiaLLMService
 
-with warnings.catch_warnings():
-    warnings.simplefilter("always")
-    warnings.warn(
-        "NimLLMService from pipecat.services.nim.llm is deprecated. "
-        "Please use NvidiaLLMService from pipecat.services.nvidia.llm instead.",
-        DeprecationWarning,
-        stacklevel=2,
-    )
+class NimLLMService(OpenAILLMService):
+    """A service for interacting with NVIDIA's NIM (NVIDIA Inference Microservice) API.
 
-NimLLMService = NvidiaLLMService
+    This service extends OpenAILLMService to work with NVIDIA's NIM API while maintaining
+    compatibility with the OpenAI-style interface. It specifically handles the difference
+    in token usage reporting between NIM (incremental) and OpenAI (final summary).
+    """
+
+    def __init__(
+        self,
+        *,
+        api_key: str,
+        base_url: str = "https://integrate.api.nvidia.com/v1",
+        model: str = "nvidia/llama-3.1-nemotron-70b-instruct",
+        **kwargs,
+    ):
+        """Initialize the NimLLMService.
+
+        Args:
+            api_key: The API key for accessing NVIDIA's NIM API.
+            base_url: The base URL for NIM API. Defaults to "https://integrate.api.nvidia.com/v1".
+            model: The model identifier to use. Defaults to "nvidia/llama-3.1-nemotron-70b-instruct".
+            **kwargs: Additional keyword arguments passed to OpenAILLMService.
+        """
+        super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
+        # Counters for accumulating token usage metrics
+        self._prompt_tokens = 0
+        self._completion_tokens = 0
+        self._total_tokens = 0
+        self._has_reported_prompt_tokens = False
+        self._is_processing = False
+
+    async def _process_context(self, context: OpenAILLMContext | LLMContext):
+        """Process a context through the LLM and accumulate token usage metrics.
+
+        This method overrides the parent class implementation to handle NVIDIA's
+        incremental token reporting style, accumulating the counts and reporting
+        them once at the end of processing.
+
+        Args:
+            context: The context to process, containing messages and other information
+                needed for the LLM interaction.
+        """
+        # Reset all counters and flags at the start of processing
+        self._prompt_tokens = 0
+        self._completion_tokens = 0
+        self._total_tokens = 0
+        self._has_reported_prompt_tokens = False
+        self._is_processing = True
+
+        try:
+            await super()._process_context(context)
+        finally:
+            self._is_processing = False
+            # Report final accumulated token usage at the end of processing
+            if self._prompt_tokens > 0 or self._completion_tokens > 0:
+                self._total_tokens = self._prompt_tokens + self._completion_tokens
+                tokens = LLMTokenUsage(
+                    prompt_tokens=self._prompt_tokens,
+                    completion_tokens=self._completion_tokens,
+                    total_tokens=self._total_tokens,
+                )
+                await super().start_llm_usage_metrics(tokens)
+
+    async def start_llm_usage_metrics(self, tokens: LLMTokenUsage):
+        """Accumulate token usage metrics during processing.
+
+        This method intercepts the incremental token updates from NVIDIA's API
+        and accumulates them instead of passing each update to the metrics system.
+        The final accumulated totals are reported at the end of processing.
+
+        Args:
+            tokens: The token usage metrics for the current chunk of processing,
+                containing prompt_tokens and completion_tokens counts.
+        """
+        # Only accumulate metrics during active processing
+        if not self._is_processing:
+            return
+
+        # Record prompt tokens the first time we see them
+        if not self._has_reported_prompt_tokens and tokens.prompt_tokens > 0:
+            self._prompt_tokens = tokens.prompt_tokens
+            self._has_reported_prompt_tokens = True
+
+        # Update completion tokens count if it has increased
+        if tokens.completion_tokens > self._completion_tokens:
+            self._completion_tokens = tokens.completion_tokens

src/pipecat/services/nvidia/llm.py

@@ -1,105 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""NVIDIA NIM API service implementation.
-
-This module provides a service for interacting with NVIDIA's NIM (NVIDIA Inference
-Microservice) API while maintaining compatibility with the OpenAI-style interface.
-"""
-
-from pipecat.metrics.metrics import LLMTokenUsage
-from pipecat.processors.aggregators.llm_context import LLMContext
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
-from pipecat.services.openai.llm import OpenAILLMService
-
-
-class NvidiaLLMService(OpenAILLMService):
-    """A service for interacting with NVIDIA's NIM (NVIDIA Inference Microservice) API.
-
-    This service extends OpenAILLMService to work with NVIDIA's NIM API while maintaining
-    compatibility with the OpenAI-style interface. It specifically handles the difference
-    in token usage reporting between NIM (incremental) and OpenAI (final summary).
-    """
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        base_url: str = "https://integrate.api.nvidia.com/v1",
-        model: str = "nvidia/llama-3.1-nemotron-70b-instruct",
-        **kwargs,
-    ):
-        """Initialize the NvidiaLLMService.
-
-        Args:
-            api_key: The API key for accessing NVIDIA's NIM API.
-            base_url: The base URL for NIM API. Defaults to "https://integrate.api.nvidia.com/v1".
-            model: The model identifier to use. Defaults to "nvidia/llama-3.1-nemotron-70b-instruct".
-            **kwargs: Additional keyword arguments passed to OpenAILLMService.
-        """
-        super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
-        # Counters for accumulating token usage metrics
-        self._prompt_tokens = 0
-        self._completion_tokens = 0
-        self._total_tokens = 0
-        self._has_reported_prompt_tokens = False
-        self._is_processing = False
-
-    async def _process_context(self, context: OpenAILLMContext | LLMContext):
-        """Process a context through the LLM and accumulate token usage metrics.
-
-        This method overrides the parent class implementation to handle NVIDIA's
-        incremental token reporting style, accumulating the counts and reporting
-        them once at the end of processing.
-
-        Args:
-            context: The context to process, containing messages and other information
-                needed for the LLM interaction.
-        """
-        # Reset all counters and flags at the start of processing
-        self._prompt_tokens = 0
-        self._completion_tokens = 0
-        self._total_tokens = 0
-        self._has_reported_prompt_tokens = False
-        self._is_processing = True
-
-        try:
-            await super()._process_context(context)
-        finally:
-            self._is_processing = False
-            # Report final accumulated token usage at the end of processing
-            if self._prompt_tokens > 0 or self._completion_tokens > 0:
-                self._total_tokens = self._prompt_tokens + self._completion_tokens
-                tokens = LLMTokenUsage(
-                    prompt_tokens=self._prompt_tokens,
-                    completion_tokens=self._completion_tokens,
-                    total_tokens=self._total_tokens,
-                )
-                await super().start_llm_usage_metrics(tokens)
-
-    async def start_llm_usage_metrics(self, tokens: LLMTokenUsage):
-        """Accumulate token usage metrics during processing.
-
-        This method intercepts the incremental token updates from NVIDIA's API
-        and accumulates them instead of passing each update to the metrics system.
-        The final accumulated totals are reported at the end of processing.
-
-        Args:
-            tokens: The token usage metrics for the current chunk of processing,
-                containing prompt_tokens and completion_tokens counts.
-        """
-        # Only accumulate metrics during active processing
-        if not self._is_processing:
-            return
-
-        # Record prompt tokens the first time we see them
-        if not self._has_reported_prompt_tokens and tokens.prompt_tokens > 0:
-            self._prompt_tokens = tokens.prompt_tokens
-            self._has_reported_prompt_tokens = True
-
-        # Update completion tokens count if it has increased
-        if tokens.completion_tokens > self._completion_tokens:
-            self._completion_tokens = tokens.completion_tokens

src/pipecat/services/nvidia/stt.py

@@ -1,663 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""NVIDIA Riva Speech-to-Text service implementations for real-time and batch transcription."""
-
-import asyncio
-from concurrent.futures import CancelledError as FuturesCancelledError
-from typing import AsyncGenerator, List, Mapping, Optional
-
-from loguru import logger
-from pydantic import BaseModel
-
-from pipecat.frames.frames import (
-    CancelFrame,
-    EndFrame,
-    ErrorFrame,
-    Frame,
-    InterimTranscriptionFrame,
-    StartFrame,
-    TranscriptionFrame,
-)
-from pipecat.services.stt_service import SegmentedSTTService, STTService
-from pipecat.transcriptions.language import Language, resolve_language
-from pipecat.utils.time import time_now_iso8601
-from pipecat.utils.tracing.service_decorators import traced_stt
-
-try:
-    import riva.client
-
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error("In order to use NVIDIA Riva STT, you need to `pip install pipecat-ai[nvidia]`.")
-    raise Exception(f"Missing module: {e}")
-
-
-def language_to_nvidia_riva_language(language: Language) -> Optional[str]:
-    """Maps Language enum to NVIDIA Riva ASR language codes.
-
-    Source:
-    https://docs.nvidia.com/deeplearning/riva/user-guide/docs/asr/asr-riva-build-table.html?highlight=fr%20fr
-
-    Args:
-        language: Language enum value.
-
-    Returns:
-        Optional[str]: NVIDIA Riva language code or None if not supported.
-    """
-    LANGUAGE_MAP = {
-        # Arabic
-        Language.AR: "ar-AR",
-        # English
-        Language.EN: "en-US",  # Default to US
-        Language.EN_US: "en-US",
-        Language.EN_GB: "en-GB",
-        # French
-        Language.FR: "fr-FR",
-        Language.FR_FR: "fr-FR",
-        # German
-        Language.DE: "de-DE",
-        Language.DE_DE: "de-DE",
-        # Hindi
-        Language.HI: "hi-IN",
-        Language.HI_IN: "hi-IN",
-        # Italian
-        Language.IT: "it-IT",
-        Language.IT_IT: "it-IT",
-        # Japanese
-        Language.JA: "ja-JP",
-        Language.JA_JP: "ja-JP",
-        # Korean
-        Language.KO: "ko-KR",
-        Language.KO_KR: "ko-KR",
-        # Portuguese
-        Language.PT: "pt-BR",  # Default to Brazilian
-        Language.PT_BR: "pt-BR",
-        # Russian
-        Language.RU: "ru-RU",
-        Language.RU_RU: "ru-RU",
-        # Spanish
-        Language.ES: "es-ES",  # Default to Spain
-        Language.ES_ES: "es-ES",
-        Language.ES_US: "es-US",  # US Spanish
-    }
-
-    return resolve_language(language, LANGUAGE_MAP, use_base_code=False)
-
-
-class NvidiaSTTService(STTService):
-    """Real-time speech-to-text service using NVIDIA Riva streaming ASR.
-
-    Provides real-time transcription capabilities using NVIDIA's Riva ASR models
-    through streaming recognition. Supports interim results and continuous audio
-    processing for low-latency applications.
-    """
-
-    class InputParams(BaseModel):
-        """Configuration parameters for NVIDIA Riva STT service.
-
-        Parameters:
-            language: Target language for transcription. Defaults to EN_US.
-        """
-
-        language: Optional[Language] = Language.EN_US
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        server: str = "grpc.nvcf.nvidia.com:443",
-        model_function_map: Mapping[str, str] = {
-            "function_id": "1598d209-5e27-4d3c-8079-4751568b1081",
-            "model_name": "parakeet-ctc-1.1b-asr",
-        },
-        sample_rate: Optional[int] = None,
-        params: Optional[InputParams] = None,
-        **kwargs,
-    ):
-        """Initialize the NVIDIA Riva STT service.
-
-        Args:
-            api_key: NVIDIA API key for authentication.
-            server: NVIDIA Riva server address. Defaults to NVIDIA Cloud Function endpoint.
-            model_function_map: Mapping containing 'function_id' and 'model_name' for the ASR model.
-            sample_rate: Audio sample rate in Hz. If None, uses pipeline default.
-            params: Additional configuration parameters for NVIDIA Riva.
-            **kwargs: Additional arguments passed to STTService.
-        """
-        super().__init__(sample_rate=sample_rate, **kwargs)
-
-        params = params or NvidiaSTTService.InputParams()
-
-        self._api_key = api_key
-        self._profanity_filter = False
-        self._automatic_punctuation = True
-        self._no_verbatim_transcripts = False
-        self._language_code = params.language
-        self._boosted_lm_words = None
-        self._boosted_lm_score = 4.0
-        self._start_history = -1
-        self._start_threshold = -1.0
-        self._stop_history = -1
-        self._stop_threshold = -1.0
-        self._stop_history_eou = -1
-        self._stop_threshold_eou = -1.0
-        self._custom_configuration = ""
-        self._function_id = model_function_map.get("function_id")
-
-        self._settings = {
-            "language": str(params.language),
-            "profanity_filter": self._profanity_filter,
-            "automatic_punctuation": self._automatic_punctuation,
-            "verbatim_transcripts": not self._no_verbatim_transcripts,
-            "boosted_lm_words": self._boosted_lm_words,
-            "boosted_lm_score": self._boosted_lm_score,
-        }
-
-        self.set_model_name(model_function_map.get("model_name"))
-
-        metadata = [
-            ["function-id", self._function_id],
-            ["authorization", f"Bearer {api_key}"],
-        ]
-        auth = riva.client.Auth(None, True, server, metadata)
-
-        self._asr_service = riva.client.ASRService(auth)
-
-        self._queue = None
-        self._config = None
-        self._thread_task = None
-        self._response_task = None
-
-    def can_generate_metrics(self) -> bool:
-        """Check if this service can generate processing metrics.
-
-        Returns:
-            False - this service does not support metrics generation.
-        """
-        return False
-
-    async def set_model(self, model: str):
-        """Set the ASR model for transcription.
-
-        Args:
-            model: Model name to set.
-
-        Note:
-            Model cannot be changed after initialization. Use model_function_map
-            parameter in constructor instead.
-        """
-        logger.warning(f"Cannot set model after initialization. Set model and function id like so:")
-        example = {"function_id": "<UUID>", "model_name": "<model_name>"}
-        logger.warning(
-            f"{self.__class__.__name__}(api_key=<api_key>, model_function_map={example})"
-        )
-
-    async def start(self, frame: StartFrame):
-        """Start the NVIDIA Riva STT service and initialize streaming configuration.
-
-        Args:
-            frame: StartFrame indicating pipeline start.
-        """
-        await super().start(frame)
-
-        if self._config:
-            return
-
-        config = riva.client.StreamingRecognitionConfig(
-            config=riva.client.RecognitionConfig(
-                encoding=riva.client.AudioEncoding.LINEAR_PCM,
-                language_code=self._language_code,
-                model="",
-                max_alternatives=1,
-                profanity_filter=self._profanity_filter,
-                enable_automatic_punctuation=self._automatic_punctuation,
-                verbatim_transcripts=not self._no_verbatim_transcripts,
-                sample_rate_hertz=self.sample_rate,
-                audio_channel_count=1,
-            ),
-            interim_results=True,
-        )
-
-        riva.client.add_word_boosting_to_config(
-            config, self._boosted_lm_words, self._boosted_lm_score
-        )
-
-        riva.client.add_endpoint_parameters_to_config(
-            config,
-            self._start_history,
-            self._start_threshold,
-            self._stop_history,
-            self._stop_history_eou,
-            self._stop_threshold,
-            self._stop_threshold_eou,
-        )
-        riva.client.add_custom_configuration_to_config(config, self._custom_configuration)
-
-        self._config = config
-        self._queue = asyncio.Queue()
-
-        if not self._thread_task:
-            self._thread_task = self.create_task(self._thread_task_handler())
-
-        if not self._response_task:
-            self._response_queue = asyncio.Queue()
-            self._response_task = self.create_task(self._response_task_handler())
-
-    async def stop(self, frame: EndFrame):
-        """Stop the NVIDIA Riva STT service and clean up resources.
-
-        Args:
-            frame: EndFrame indicating pipeline stop.
-        """
-        await super().stop(frame)
-        await self._stop_tasks()
-
-    async def cancel(self, frame: CancelFrame):
-        """Cancel the NVIDIA Riva STT service operation.
-
-        Args:
-            frame: CancelFrame indicating operation cancellation.
-        """
-        await super().cancel(frame)
-        await self._stop_tasks()
-
-    async def _stop_tasks(self):
-        if self._thread_task:
-            await self.cancel_task(self._thread_task)
-            self._thread_task = None
-
-        if self._response_task:
-            await self.cancel_task(self._response_task)
-            self._response_task = None
-
-    def _response_handler(self):
-        responses = self._asr_service.streaming_response_generator(
-            audio_chunks=self,
-            streaming_config=self._config,
-        )
-        for response in responses:
-            if not response.results:
-                continue
-            asyncio.run_coroutine_threadsafe(
-                self._response_queue.put(response), self.get_event_loop()
-            )
-
-    async def _thread_task_handler(self):
-        try:
-            self._thread_running = True
-            await asyncio.to_thread(self._response_handler)
-        except asyncio.CancelledError:
-            self._thread_running = False
-            raise
-
-    @traced_stt
-    async def _handle_transcription(
-        self, transcript: str, is_final: bool, language: Optional[Language] = None
-    ):
-        """Handle a transcription result with tracing."""
-        pass
-
-    async def _handle_response(self, response):
-        for result in response.results:
-            if result and not result.alternatives:
-                continue
-
-            transcript = result.alternatives[0].transcript
-            if transcript and len(transcript) > 0:
-                await self.stop_ttfb_metrics()
-                if result.is_final:
-                    await self.stop_processing_metrics()
-                    await self.push_frame(
-                        TranscriptionFrame(
-                            transcript,
-                            self._user_id,
-                            time_now_iso8601(),
-                            self._language_code,
-                            result=result,
-                        )
-                    )
-                    await self._handle_transcription(
-                        transcript=transcript,
-                        is_final=result.is_final,
-                        language=self._language_code,
-                    )
-                else:
-                    await self.push_frame(
-                        InterimTranscriptionFrame(
-                            transcript,
-                            self._user_id,
-                            time_now_iso8601(),
-                            self._language_code,
-                            result=result,
-                        )
-                    )
-
-    async def _response_task_handler(self):
-        while True:
-            response = await self._response_queue.get()
-            await self._handle_response(response)
-            self._response_queue.task_done()
-
-    async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
-        """Process audio data for speech-to-text transcription.
-
-        Args:
-            audio: Raw audio bytes to transcribe.
-
-        Yields:
-            None - transcription results are pushed to the pipeline via frames.
-        """
-        await self.start_ttfb_metrics()
-        await self.start_processing_metrics()
-        await self._queue.put(audio)
-        yield None
-
-    def __next__(self) -> bytes:
-        """Get the next audio chunk for NVIDIA Riva processing.
-
-        Returns:
-            Audio bytes from the queue.
-
-        Raises:
-            StopIteration: When the thread is no longer running.
-        """
-        if not self._thread_running:
-            raise StopIteration
-
-        try:
-            future = asyncio.run_coroutine_threadsafe(self._queue.get(), self.get_event_loop())
-            return future.result()
-        except FuturesCancelledError:
-            raise StopIteration
-
-    def __iter__(self):
-        """Return iterator for audio chunk processing.
-
-        Returns:
-            Self as iterator.
-        """
-        return self
-
-
-class NvidiaSegmentedSTTService(SegmentedSTTService):
-    """Speech-to-text service using NVIDIA Riva's offline/batch models.
-
-    By default, his service uses NVIDIA's Riva Canary ASR API to perform speech-to-text
-    transcription on audio segments. It inherits from SegmentedSTTService to handle
-    audio buffering and speech detection.
-    """
-
-    class InputParams(BaseModel):
-        """Configuration parameters for NVIDIA Riva segmented STT service.
-
-        Parameters:
-            language: Target language for transcription. Defaults to EN_US.
-            profanity_filter: Whether to filter profanity from results.
-            automatic_punctuation: Whether to add automatic punctuation.
-            verbatim_transcripts: Whether to return verbatim transcripts.
-            boosted_lm_words: List of words to boost in language model.
-            boosted_lm_score: Score boost for specified words.
-        """
-
-        language: Optional[Language] = Language.EN_US
-        profanity_filter: bool = False
-        automatic_punctuation: bool = True
-        verbatim_transcripts: bool = False
-        boosted_lm_words: Optional[List[str]] = None
-        boosted_lm_score: float = 4.0
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        server: str = "grpc.nvcf.nvidia.com:443",
-        model_function_map: Mapping[str, str] = {
-            "function_id": "ee8dc628-76de-4acc-8595-1836e7e857bd",
-            "model_name": "canary-1b-asr",
-        },
-        sample_rate: Optional[int] = None,
-        params: Optional[InputParams] = None,
-        **kwargs,
-    ):
-        """Initialize the NVIDIA Riva segmented STT service.
-
-        Args:
-            api_key: NVIDIA API key for authentication
-            server: NVIDIA Riva server address (defaults to NVIDIA Cloud Function endpoint)
-            model_function_map: Mapping of model name and its corresponding NVIDIA Cloud Function ID
-            sample_rate: Audio sample rate in Hz. If not provided, uses the pipeline's rate
-            params: Additional configuration parameters for NVIDIA Riva
-            **kwargs: Additional arguments passed to SegmentedSTTService
-        """
-        super().__init__(sample_rate=sample_rate, **kwargs)
-
-        params = params or NvidiaSegmentedSTTService.InputParams()
-
-        # Set model name
-        self.set_model_name(model_function_map.get("model_name"))
-
-        # Initialize NVIDIA Riva settings
-        self._api_key = api_key
-        self._server = server
-        self._function_id = model_function_map.get("function_id")
-        self._model_name = model_function_map.get("model_name")
-
-        # Store the language as a Language enum and as a string
-        self._language_enum = params.language or Language.EN_US
-        self._language = self.language_to_service_language(self._language_enum) or "en-US"
-
-        # Configure transcription parameters
-        self._profanity_filter = params.profanity_filter
-        self._automatic_punctuation = params.automatic_punctuation
-        self._verbatim_transcripts = params.verbatim_transcripts
-        self._boosted_lm_words = params.boosted_lm_words
-        self._boosted_lm_score = params.boosted_lm_score
-
-        # Voice activity detection thresholds (use NVIDIA Riva defaults)
-        self._start_history = -1
-        self._start_threshold = -1.0
-        self._stop_history = -1
-        self._stop_threshold = -1.0
-        self._stop_history_eou = -1
-        self._stop_threshold_eou = -1.0
-        self._custom_configuration = ""
-
-        # Create NVIDIA Riva client
-        self._config = None
-        self._asr_service = None
-        self._settings = {"language": self._language_enum}
-
-    def language_to_service_language(self, language: Language) -> Optional[str]:
-        """Convert pipecat Language enum to NVIDIA Riva's language code.
-
-        Args:
-            language: Language enum value.
-
-        Returns:
-            NVIDIA Riva language code or None if not supported.
-        """
-        return language_to_nvidia_riva_language(language)
-
-    def _initialize_client(self):
-        """Initialize the NVIDIA Riva ASR client with authentication metadata."""
-        if self._asr_service is not None:
-            return
-
-        # Set up authentication metadata for NVIDIA Cloud Functions
-        metadata = [
-            ["function-id", self._function_id],
-            ["authorization", f"Bearer {self._api_key}"],
-        ]
-
-        # Create authenticated client
-        auth = riva.client.Auth(None, True, self._server, metadata)
-        self._asr_service = riva.client.ASRService(auth)
-
-        logger.info(f"Initialized NvidiaSegmentedSTTService with model: {self.model_name}")
-
-    def _create_recognition_config(self):
-        """Create the NVIDIA Riva ASR recognition configuration."""
-        # Create base configuration
-        config = riva.client.RecognitionConfig(
-            language_code=self._language,  # Now using the string, not a tuple
-            max_alternatives=1,
-            profanity_filter=self._profanity_filter,
-            enable_automatic_punctuation=self._automatic_punctuation,
-            verbatim_transcripts=self._verbatim_transcripts,
-        )
-
-        # Add word boosting if specified
-        if self._boosted_lm_words:
-            riva.client.add_word_boosting_to_config(
-                config, self._boosted_lm_words, self._boosted_lm_score
-            )
-
-        # Add voice activity detection parameters
-        riva.client.add_endpoint_parameters_to_config(
-            config,
-            self._start_history,
-            self._start_threshold,
-            self._stop_history,
-            self._stop_history_eou,
-            self._stop_threshold,
-            self._stop_threshold_eou,
-        )
-
-        # Add any custom configuration
-        if self._custom_configuration:
-            riva.client.add_custom_configuration_to_config(config, self._custom_configuration)
-
-        return config
-
-    def can_generate_metrics(self) -> bool:
-        """Check if this service can generate processing metrics.
-
-        Returns:
-            True - this service supports metrics generation.
-        """
-        return True
-
-    async def set_model(self, model: str):
-        """Set the ASR model for transcription.
-
-        Args:
-            model: Model name to set.
-
-        Note:
-            Model cannot be changed after initialization. Use model_function_map
-            parameter in constructor instead.
-        """
-        logger.warning(f"Cannot set model after initialization. Set model and function id like so:")
-        example = {"function_id": "<UUID>", "model_name": "<model_name>"}
-        logger.warning(
-            f"{self.__class__.__name__}(api_key=<api_key>, model_function_map={example})"
-        )
-
-    async def start(self, frame: StartFrame):
-        """Initialize the service when the pipeline starts.
-
-        Args:
-            frame: StartFrame indicating pipeline start.
-        """
-        await super().start(frame)
-        self._initialize_client()
-        self._config = self._create_recognition_config()
-
-    async def set_language(self, language: Language):
-        """Set the language for the STT service.
-
-        Args:
-            language: Target language for transcription.
-        """
-        logger.info(f"Switching STT language to: [{language}]")
-        self._language_enum = language
-        self._language = self.language_to_service_language(language) or "en-US"
-        self._settings["language"] = language
-
-        # Update configuration with new language
-        if self._config:
-            self._config.language_code = self._language
-
-    @traced_stt
-    async def _handle_transcription(
-        self, transcript: str, is_final: bool, language: Optional[Language] = None
-    ):
-        """Handle a transcription result with tracing."""
-        pass
-
-    async def run_stt(self, audio: bytes) -> AsyncGenerator[Frame, None]:
-        """Transcribe an audio segment.
-
-        Args:
-            audio: Raw audio bytes in WAV format (already converted by base class).
-
-        Yields:
-            Frame: TranscriptionFrame containing the transcribed text.
-        """
-        try:
-            await self.start_processing_metrics()
-            await self.start_ttfb_metrics()
-
-            # Make sure the client is initialized
-            if self._asr_service is None:
-                self._initialize_client()
-
-            # Make sure the config is created
-            if self._config is None:
-                self._config = self._create_recognition_config()
-
-            # Type assertion to satisfy the IDE
-            assert self._asr_service is not None, "ASR service not initialized"
-            assert self._config is not None, "Recognition config not created"
-
-            # Process audio with NVIDIA Riva ASR - explicitly request non-future response
-            raw_response = self._asr_service.offline_recognize(audio, self._config, future=False)
-
-            await self.stop_ttfb_metrics()
-            await self.stop_processing_metrics()
-
-            # Process the response - handle different possible return types
-            try:
-                # If it's a future-like object, get the result
-                if hasattr(raw_response, "result"):
-                    response = raw_response.result()
-                else:
-                    response = raw_response
-
-                # Process transcription results
-                transcription_found = False
-
-                # Now we can safely check results
-                # Type hint for the IDE
-                results = getattr(response, "results", [])
-
-                for result in results:
-                    alternatives = getattr(result, "alternatives", [])
-                    if alternatives:
-                        text = alternatives[0].transcript.strip()
-                        if text:
-                            logger.debug(f"Transcription: [{text}]")
-                            yield TranscriptionFrame(
-                                text,
-                                self._user_id,
-                                time_now_iso8601(),
-                                self._language_enum,
-                            )
-                            transcription_found = True
-
-                            await self._handle_transcription(text, True, self._language_enum)
-
-                if not transcription_found:
-                    logger.debug("No transcription results found in NVIDIA Riva response")
-
-            except AttributeError as ae:
-                logger.error(f"Unexpected response structure from NVIDIA Riva: {ae}")
-                yield ErrorFrame(f"Unexpected NVIDIA Riva response format: {str(ae)}")
-
-        except Exception as e:
-            logger.error(f"{self} exception: {e}")
-            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/nvidia/tts.py

@@ -1,187 +0,0 @@
-#
-# Copyright (c) 2024–2025, Daily
-#
-# SPDX-License-Identifier: BSD 2-Clause License
-#
-
-"""NVIDIA Riva text-to-speech service implementation.
-
-This module provides integration with NVIDIA Riva's TTS services through
-gRPC API for high-quality speech synthesis.
-"""
-
-import asyncio
-import os
-from typing import AsyncGenerator, Mapping, Optional
-
-from pipecat.utils.tracing.service_decorators import traced_tts
-
-# Suppress gRPC fork warnings
-os.environ["GRPC_ENABLE_FORK_SUPPORT"] = "false"
-
-from loguru import logger
-from pydantic import BaseModel
-
-from pipecat.frames.frames import (
-    ErrorFrame,
-    Frame,
-    TTSAudioRawFrame,
-    TTSStartedFrame,
-    TTSStoppedFrame,
-)
-from pipecat.services.tts_service import TTSService
-from pipecat.transcriptions.language import Language
-
-try:
-    import riva.client
-
-except ModuleNotFoundError as e:
-    logger.error(f"Exception: {e}")
-    logger.error("In order to use NVIDIA Riva TTS, you need to `pip install pipecat-ai[nvidia]`.")
-    raise Exception(f"Missing module: {e}")
-
-NVIDIA_TTS_TIMEOUT_SECS = 5
-
-
-class NvidiaTTSService(TTSService):
-    """NVIDIA Riva text-to-speech service.
-
-    Provides high-quality text-to-speech synthesis using NVIDIA Riva's
-    cloud-based TTS models. Supports multiple voices, languages, and
-    configurable quality settings.
-    """
-
-    class InputParams(BaseModel):
-        """Input parameters for Riva TTS configuration.
-
-        Parameters:
-            language: Language code for synthesis. Defaults to US English.
-            quality: Audio quality setting (0-100). Defaults to 20.
-        """
-
-        language: Optional[Language] = Language.EN_US
-        quality: Optional[int] = 20
-
-    def __init__(
-        self,
-        *,
-        api_key: str,
-        server: str = "grpc.nvcf.nvidia.com:443",
-        voice_id: str = "Magpie-Multilingual.EN-US.Aria",
-        sample_rate: Optional[int] = None,
-        model_function_map: Mapping[str, str] = {
-            "function_id": "877104f7-e885-42b9-8de8-f6e4c6303969",
-            "model_name": "magpie-tts-multilingual",
-        },
-        params: Optional[InputParams] = None,
-        **kwargs,
-    ):
-        """Initialize the NVIDIA Riva TTS service.
-
-        Args:
-            api_key: NVIDIA API key for authentication.
-            server: gRPC server endpoint. Defaults to NVIDIA's cloud endpoint.
-            voice_id: Voice model identifier. Defaults to multilingual Ray voice.
-            sample_rate: Audio sample rate. If None, uses service default.
-            model_function_map: Dictionary containing function_id and model_name for the TTS model.
-            params: Additional configuration parameters for TTS synthesis.
-            **kwargs: Additional arguments passed to parent TTSService.
-        """
-        super().__init__(sample_rate=sample_rate, **kwargs)
-
-        params = params or NvidiaTTSService.InputParams()
-
-        self._api_key = api_key
-        self._voice_id = voice_id
-        self._language_code = params.language
-        self._quality = params.quality
-        self._function_id = model_function_map.get("function_id")
-
-        self.set_model_name(model_function_map.get("model_name"))
-        self.set_voice(voice_id)
-
-        metadata = [
-            ["function-id", self._function_id],
-            ["authorization", f"Bearer {api_key}"],
-        ]
-        auth = riva.client.Auth(None, True, server, metadata)
-
-        self._service = riva.client.SpeechSynthesisService(auth)
-
-        # warm up the service
-        config_response = self._service.stub.GetRivaSynthesisConfig(
-            riva.client.proto.riva_tts_pb2.RivaSynthesisConfigRequest()
-        )
-
-    async def set_model(self, model: str):
-        """Attempt to set the TTS model.
-
-        Note: Model cannot be changed after initialization for Riva service.
-
-        Args:
-            model: The model name to set (operation not supported).
-        """
-        logger.warning(f"Cannot set model after initialization. Set model and function id like so:")
-        example = {"function_id": "<UUID>", "model_name": "<model_name>"}
-        logger.warning(
-            f"{self.__class__.__name__}(api_key=<api_key>, model_function_map={example})"
-        )
-
-    @traced_tts
-    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
-        """Generate speech from text using NVIDIA Riva TTS.
-
-        Args:
-            text: The text to synthesize into speech.
-
-        Yields:
-            Frame: Audio frames containing the synthesized speech data.
-        """
-
-        def read_audio_responses(queue: asyncio.Queue):
-            def add_response(r):
-                asyncio.run_coroutine_threadsafe(queue.put(r), self.get_event_loop())
-
-            try:
-                responses = self._service.synthesize_online(
-                    text,
-                    self._voice_id,
-                    self._language_code,
-                    sample_rate_hz=self.sample_rate,
-                    zero_shot_audio_prompt_file=None,
-                    zero_shot_quality=self._quality,
-                    custom_dictionary={},
-                )
-                for r in responses:
-                    add_response(r)
-                add_response(None)
-            except Exception as e:
-                logger.error(f"{self} exception: {e}")
-                add_response(None)
-
-        await self.start_ttfb_metrics()
-        yield TTSStartedFrame()
-
-        logger.debug(f"{self}: Generating TTS [{text}]")
-
-        try:
-            queue = asyncio.Queue()
-            await asyncio.to_thread(read_audio_responses, queue)
-
-            # Wait for the thread to start.
-            resp = await asyncio.wait_for(queue.get(), timeout=NVIDIA_TTS_TIMEOUT_SECS)
-            while resp:
-                await self.stop_ttfb_metrics()
-                frame = TTSAudioRawFrame(
-                    audio=resp.audio,
-                    sample_rate=self.sample_rate,
-                    num_channels=1,
-                )
-                yield frame
-                resp = await asyncio.wait_for(queue.get(), timeout=NVIDIA_TTS_TIMEOUT_SECS)
-        except asyncio.TimeoutError:
-            logger.error(f"{self} timeout waiting for audio response")
-            yield ErrorFrame(error=f"{self} error: {e}")
-
-        await self.start_tts_usage_metrics(text)
-        yield TTSStoppedFrame()

src/pipecat/services/openai/base_llm.py

@@ -133,7 +133,6 @@ class BaseOpenAILLMService(LLMService):
         self._retry_timeout_secs = retry_timeout_secs
         self._retry_on_timeout = retry_on_timeout
         self.set_model_name(model)
-        self._full_model_name: str = ""
         self._client = self.create_client(
             api_key=api_key,
             base_url=base_url,
@@ -186,22 +185,6 @@ class BaseOpenAILLMService(LLMService):
         """
         return True
 
-    def set_full_model_name(self, full_model_name: str):
-        """Set the full AI model name.
-
-        Args:
-            full_model_name: The full name of the AI model to use.
-        """
-        self._full_model_name = full_model_name
-
-    def get_full_model_name(self):
-        """Get the current full model name.
-
-        Returns:
-            The full name of the AI model being used.
-        """
-        return self._full_model_name
-
     async def get_chat_completions(
         self, params_from_context: OpenAILLMInvocationParams
     ) -> AsyncStream[ChatCompletionChunk]:
@@ -363,23 +346,14 @@ class BaseOpenAILLMService(LLMService):
                     if chunk.usage.prompt_tokens_details
                     else None
                 )
-                reasoning_tokens = (
-                    chunk.usage.completion_tokens_details.reasoning_tokens
-                    if chunk.usage.completion_tokens_details
-                    else None
-                )
                 tokens = LLMTokenUsage(
                     prompt_tokens=chunk.usage.prompt_tokens,
                     completion_tokens=chunk.usage.completion_tokens,
                     total_tokens=chunk.usage.total_tokens,
                     cache_read_input_tokens=cached_tokens,
-                    reasoning_tokens=reasoning_tokens,
                 )
                 await self.start_llm_usage_metrics(tokens)
 
-            if chunk.model and self.get_full_model_name() != chunk.model:
-                self.set_full_model_name(chunk.model)
-
             if chunk.choices is None or len(chunk.choices) == 0:
                 continue
 

src/pipecat/services/openai/image.py

@@ -76,6 +76,7 @@ class OpenAIImageGenService(ImageGenService):
         image_url = image.data[0].url
 
         if not image_url:
+            logger.error(f"{self} No image provided in response: {image}")
             yield ErrorFrame("Image generation failed")
             return
 

src/pipecat/services/openai/realtime/llm.py

@@ -57,6 +57,7 @@ from pipecat.processors.aggregators.openai_llm_context import (
 )
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.services.llm_service import FunctionCallFromLLM, LLMService
+from pipecat.services.openai.llm import OpenAIContextAggregatorPair
 from pipecat.transcriptions.language import Language
 from pipecat.utils.time import time_now_iso8601
 from pipecat.utils.tracing.service_decorators import traced_openai_realtime, traced_stt
@@ -443,7 +444,7 @@ class OpenAIRealtimeLLMService(LLMService):
             )
             self._receive_task = self.create_task(self._receive_task_handler())
         except Exception as e:
-            await self.push_error(error_msg=f"Error connecting: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             self._websocket = None
 
     async def _disconnect(self):
@@ -460,7 +461,7 @@ class OpenAIRealtimeLLMService(LLMService):
             self._completed_tool_calls = set()
             self._disconnecting = False
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} error disconnecting: {e}")
 
     async def _ws_send(self, realtime_message):
         try:
@@ -473,11 +474,12 @@ class OpenAIRealtimeLLMService(LLMService):
                 # somehow *started* the websocket send attempt while we still
                 # had a connection)
                 return
+            logger.error(f"Error sending message to websocket: {e}")
             # In server-to-server contexts, a WebSocket error should be quite rare. Given how hard
             # it is to recover from a send-side error with proper state management, and that exponential
             # backoff for retries can have cost/stability implications for a service cluster, let's just
             # treat a send-side error as fatal.
-            await self.push_error(error_msg=f"Error sending client event: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"Error sending client event: {e}"))
 
     async def _update_settings(self):
         settings = self._session_properties
@@ -655,17 +657,10 @@ class OpenAIRealtimeLLMService(LLMService):
     async def _handle_evt_response_done(self, evt):
         # todo: figure out whether there's anything we need to do for "cancelled" events
         # usage metrics
-        cached_tokens = (
-            evt.response.usage.input_token_details.cached_tokens
-            if hasattr(evt.response.usage, "input_token_details")
-            and evt.response.usage.input_token_details
-            else None
-        )
         tokens = LLMTokenUsage(
             prompt_tokens=evt.response.usage.input_tokens,
             completion_tokens=evt.response.usage.output_tokens,
             total_tokens=evt.response.usage.total_tokens,
-            cache_read_input_tokens=cached_tokens,
         )
         await self.start_llm_usage_metrics(tokens)
         await self.stop_processing_metrics()
@@ -673,7 +668,7 @@ class OpenAIRealtimeLLMService(LLMService):
         self._current_assistant_response = None
         # error handling
         if evt.response.status == "failed":
-            await self.push_error(error_msg=evt.response.status_details["error"]["message"])
+            await self.push_error(ErrorFrame(error=evt.response.status_details["error"]["message"]))
             return
         # response content
         for item in evt.response.output:
@@ -765,7 +760,7 @@ class OpenAIRealtimeLLMService(LLMService):
 
     async def _handle_evt_error(self, evt):
         # Errors are fatal to this connection. Send an ErrorFrame.
-        await self.push_error(error_msg=f"Error: {evt}")
+        await self.push_error(ErrorFrame(error=f"Error: {evt}"))
 
     #
     # state and client events for the current conversation
@@ -815,7 +810,7 @@ class OpenAIRealtimeLLMService(LLMService):
             # We're done configuring the LLM for this session
             self._llm_needs_conversation_setup = False
 
-        logger.debug("Creating response")
+        logger.debug(f"Creating response")
 
         await self.push_frame(LLMFullResponseStartFrame())
         await self.start_processing_metrics()

src/pipecat/services/openai/tts.py

@@ -206,4 +206,5 @@ class OpenAITTSService(TTSService):
                         yield frame
                 yield TTSStoppedFrame()
         except BadRequestError as e:
-            yield ErrorFrame(error=f"Unknown error occurred: {e}")
+            logger.exception(f"{self} error generating TTS: {e}")
+            yield ErrorFrame(error=f"{self} error: {e}")

src/pipecat/services/openai_realtime_beta/azure.py

@@ -79,5 +79,5 @@ class AzureRealtimeBetaLLMService(OpenAIRealtimeBetaLLMService):
             )
             self._receive_task = self.create_task(self._receive_task_handler())
         except Exception as e:
-            await self.push_error(error_msg=f"Error connecting: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             self._websocket = None

src/pipecat/services/openai_realtime_beta/openai.py

@@ -425,7 +425,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
             )
             self._receive_task = self.create_task(self._receive_task_handler())
         except Exception as e:
-            await self.push_error(error_msg=f"Error connecting: {e}", exception=e)
+            logger.error(f"{self} initialization error: {e}")
             self._websocket = None
 
     async def _disconnect(self):
@@ -441,7 +441,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
                 self._receive_task = None
             self._disconnecting = False
         except Exception as e:
-            await self.push_error(error_msg=f"Error disconnecting: {e}", exception=e)
+            logger.error(f"{self} error disconnecting: {e}")
 
     async def _ws_send(self, realtime_message):
         try:
@@ -450,11 +450,12 @@ class OpenAIRealtimeBetaLLMService(LLMService):
         except Exception as e:
             if self._disconnecting:
                 return
+            logger.error(f"Error sending message to websocket: {e}")
             # In server-to-server contexts, a WebSocket error should be quite rare. Given how hard
             # it is to recover from a send-side error with proper state management, and that exponential
             # backoff for retries can have cost/stability implications for a service cluster, let's just
             # treat a send-side error as fatal.
-            await self.push_error(error_msg=f"Error sending client event: {e}", exception=e)
+            await self.push_error(ErrorFrame(error=f"Error sending client event: {e}"))
 
     async def _update_settings(self):
         settings = self._session_properties
@@ -685,7 +686,7 @@ class OpenAIRealtimeBetaLLMService(LLMService):
 
     async def _handle_evt_error(self, evt):
         # Errors are fatal to this connection. Send an ErrorFrame.
-        await self.push_error(error_msg=f"Error: {evt}")
+        await self.push_error(ErrorFrame(error=f"Error: {evt}"))
 
     async def _handle_assistant_output(self, output):
         # We haven't seen intermixed audio and function_call items in the same response. But let's