fix unit tests
This commit is contained in:
gui217
@@ -1,136 +1,129 @@
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import asyncio
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import sys
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from unittest.mock import MagicMock
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import unittest
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from unittest.mock import MagicMock, patch
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import numpy as np
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import pytest
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# Mock pyrnnoise BEFORE importing RNNoiseFilter
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mock_pyrnnoise = MagicMock()
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mock_rnnoise_class = MagicMock()
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mock_pyrnnoise.RNNoise = mock_rnnoise_class
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sys.modules["pyrnnoise"] = mock_pyrnnoise
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# Now import the filter
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# We don't need to mock sys.modules here if we use patch on the imported module member
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# But we need to ensure RNNoiseFilter is imported so we can patch its member
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try:
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from pipecat.audio.filters.rnnoise_filter import RNNoiseFilter
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from pipecat.frames.frames import FilterEnableFrame
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except ImportError as e:
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# If dependencies are missing (like numpy?), we can't test
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print(f"Failed to import RNNoiseFilter: {e}")
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sys.exit(1)
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async def test_rnnoise_resampling_16k_to_48k_and_back():
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print("\nStarting Resampling Test: 16kHz -> 48kHz -> 16kHz")
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class TestRNNoiseResampling(unittest.IsolatedAsyncioTestCase):
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@patch("pipecat.audio.filters.rnnoise_filter.RNNoise")
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async def test_rnnoise_resampling_16k_to_48k_and_back(self, mock_rnnoise_class):
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print("\nStarting Resampling Test: 16kHz -> 48kHz -> 16kHz")
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# Configure Mock with buffering behavior
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processed_chunks_count = 0
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buffer = np.array([], dtype=np.int16)
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# Configure Mock with buffering behavior
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processed_chunks_count = 0
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buffer = np.array([], dtype=np.int16)
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def side_effect_process_chunk(audio_samples, partial=False):
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nonlocal buffer, processed_chunks_count
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def side_effect_process_chunk(audio_samples, partial=False):
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nonlocal buffer, processed_chunks_count
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# Append new samples to buffer
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if len(audio_samples) > 0:
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buffer = np.concatenate((buffer, audio_samples))
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# Append new samples to buffer
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if len(audio_samples) > 0:
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buffer = np.concatenate((buffer, audio_samples))
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# Yield 480-sample chunks
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while len(buffer) >= 480:
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chunk = buffer[:480]
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buffer = buffer[480:]
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processed_chunks_count += 1
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# Yield 480-sample chunks
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while len(buffer) >= 480:
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chunk = buffer[:480]
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buffer = buffer[480:]
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processed_chunks_count += 1
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# Simulate processing (pass through)
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# Convert int16 -> float32 [-1, 1]
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normalized = chunk.astype(np.float32) / 32768.0
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yield 0.99, normalized
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# Simulate processing (pass through)
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# Convert int16 -> float32 [-1, 1]
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normalized = chunk.astype(np.float32) / 32768.0
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yield 0.99, normalized
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mock_rnnoise_instance = MagicMock()
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mock_rnnoise_instance.denoise_chunk.side_effect = side_effect_process_chunk
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mock_rnnoise_class.return_value = mock_rnnoise_instance
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mock_rnnoise_instance = MagicMock()
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mock_rnnoise_instance.denoise_chunk.side_effect = side_effect_process_chunk
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mock_rnnoise_class.return_value = mock_rnnoise_instance
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# 1. Generate 1 second of 16kHz audio (sine wave 440Hz)
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sample_rate = 16000
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duration = 1.0
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t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
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audio_data = (np.sin(2 * np.pi * 440 * t) * 32767).astype(np.int16)
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audio_bytes = audio_data.tobytes()
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# 1. Generate 1 second of 16kHz audio (sine wave 440Hz)
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sample_rate = 16000
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duration = 1.0
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t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False)
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audio_data = (np.sin(2 * np.pi * 440 * t) * 32767).astype(np.int16)
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audio_bytes = audio_data.tobytes()
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print(f"Input audio: {len(audio_bytes)} bytes, {len(audio_data)} samples at {sample_rate}Hz")
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print(
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f"Input audio: {len(audio_bytes)} bytes, {len(audio_data)} samples at {sample_rate}Hz"
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)
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# 2. Initialize RNNoiseFilter
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rnnoise_filter = RNNoiseFilter()
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await rnnoise_filter.start(sample_rate)
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# 2. Initialize RNNoiseFilter
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# This will use the patched RNNoise
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rnnoise_filter = RNNoiseFilter()
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await rnnoise_filter.start(sample_rate)
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# Enable filtering
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await rnnoise_filter.process_frame(FilterEnableFrame(enable=True))
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# Enable filtering
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await rnnoise_filter.process_frame(FilterEnableFrame(enable=True))
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# 3. Process audio in chunks
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chunk_size = 320 # 160 samples (10ms at 16k) * 2 bytes
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processed_audio = b""
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# 3. Process audio in chunks
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chunk_size = 320 # 160 samples (10ms at 16k) * 2 bytes
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processed_audio = b""
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for i in range(0, len(audio_bytes), chunk_size):
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chunk = audio_bytes[i : i + chunk_size]
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result = await rnnoise_filter.filter(chunk)
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processed_audio += result
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for i in range(0, len(audio_bytes), chunk_size):
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chunk = audio_bytes[i : i + chunk_size]
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result = await rnnoise_filter.filter(chunk)
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processed_audio += result
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await rnnoise_filter.stop()
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await rnnoise_filter.stop()
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print(f"Output audio: {len(processed_audio)} bytes")
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print(f"Processed chunks (internal 480 samples): {processed_chunks_count}")
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print(f"Output audio: {len(processed_audio)} bytes")
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print(f"Processed chunks (internal 480 samples): {processed_chunks_count}")
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# 4. Verify output length
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# Expect roughly same length
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# Input: 16000 samples.
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# Upsampled to 48000.
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# 48000 / 480 = 100 chunks.
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# So we expect roughly 100 calls to process_chunk.
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expected_chunks = (len(audio_data) * 48000 // sample_rate) // 480
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print(f"Expected chunks: ~{expected_chunks}")
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# 4. Verify output length
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# Expect roughly same length
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expected_chunks = (len(audio_data) * 48000 // sample_rate) // 480
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print(f"Expected chunks: ~{expected_chunks}")
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# Check that we actually processed something
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assert processed_chunks_count >= expected_chunks - 5, "Too few chunks processed"
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# Check that we actually processed something
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self.assertGreaterEqual(
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processed_chunks_count, expected_chunks - 5, "Too few chunks processed"
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)
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# Check output length
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assert len(processed_audio) > 0, "Output should not be empty"
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# Check output length
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self.assertGreater(len(processed_audio), 0, "Output should not be empty")
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# Check length matches input (with some tolerance for buffering latency)
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# Since we don't flush the filter explicitly (no flush method in RNNoiseFilter yet),
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# some data might remain in buffers.
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# Max loss:
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# - Resampler input buffer
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# - RNNoise buffer (max 480 samples = 10ms)
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# - Resampler output buffer
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# Check length matches input (with some tolerance for buffering latency)
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# 100ms tolerance?
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byte_tolerance = int(0.2 * sample_rate * 2)
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self.assertGreaterEqual(
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len(processed_audio),
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len(audio_bytes) - byte_tolerance,
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f"Output too short: {len(processed_audio)} vs {len(audio_bytes)}",
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)
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self.assertLessEqual(
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len(processed_audio),
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len(audio_bytes) + byte_tolerance,
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f"Output too long: {len(processed_audio)} vs {len(audio_bytes)}",
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)
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# 100ms tolerance?
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byte_tolerance = int(0.2 * sample_rate * 2)
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assert len(processed_audio) >= len(audio_bytes) - byte_tolerance, (
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f"Output too short: {len(processed_audio)} vs {len(audio_bytes)}"
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)
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assert len(processed_audio) <= len(audio_bytes) + byte_tolerance, (
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f"Output too long: {len(processed_audio)} vs {len(audio_bytes)}"
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)
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# 5. Check sample rate / pitch preservation
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output_data = np.frombuffer(processed_audio, dtype=np.int16)
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# 5. Check sample rate / pitch preservation
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# If we upsampled and downsampled correctly, the pitch should be 440Hz.
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output_data = np.frombuffer(processed_audio, dtype=np.int16)
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if len(output_data) > 2000:
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# Use a window in the middle
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start_idx = len(output_data) // 4
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end_idx = 3 * len(output_data) // 4
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segment = output_data[start_idx:end_idx]
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if len(output_data) > 2000:
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# Use a window in the middle
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start_idx = len(output_data) // 4
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end_idx = 3 * len(output_data) // 4
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segment = output_data[start_idx:end_idx]
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fft = np.fft.rfft(segment)
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freqs = np.fft.rfftfreq(len(segment), d=1 / sample_rate)
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peak_idx = np.argmax(np.abs(fft))
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peak_freq = freqs[peak_idx]
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fft = np.fft.rfft(segment)
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freqs = np.fft.rfftfreq(len(segment), d=1 / sample_rate)
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peak_idx = np.argmax(np.abs(fft))
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peak_freq = freqs[peak_idx]
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print(f"Peak frequency: {peak_freq:.2f} Hz")
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self.assertLess(
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abs(peak_freq - 440), 50, f"Frequency shifted significantly: {peak_freq} vs 440"
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)
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print(f"Peak frequency: {peak_freq:.2f} Hz")
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assert abs(peak_freq - 440) < 50, f"Frequency shifted significantly: {peak_freq} vs 440"
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print("Test Passed: Resampling logic verified (with mocked RNNoise).")
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if __name__ == "__main__":
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asyncio.run(test_rnnoise_resampling_16k_to_48k_and_back())
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print("Test Passed: Resampling logic verified (with mocked RNNoise).")
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