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Testing Audio Processing Components in Coqui-AI TTS

This test suite validates the AudioProcessor class functionality in the Coqui-AI TTS library, focusing on audio processing operations like normalization, mel-spectrogram generation, and F0 computation. The tests ensure reliable audio transformations and consistent processing across different normalization parameters.

Test Coverage Overview

The test suite provides comprehensive coverage of audio processing operations:

Audio synthesis workflow testing (wav → mel → wav conversion)
Normalization and denormalization validation
Scaler functionality verification
F0 computation accuracy checks
Edge cases for different normalization parameters

Implementation Analysis

The testing approach utilizes unittest framework with systematic validation of AudioProcessor methods. It implements parametrized testing patterns for normalization configurations and employs assertion-based verification for numerical accuracy. The tests use real audio files and verify both forward and inverse transformations.

Technical Details

Testing Framework: Python unittest
Key Dependencies: TTS.utils.audio.processor
Test Data: Example WAV files
Configuration: BaseAudioConfig with customizable parameters
Output Validation: Numerical assertion checks

Best Practices Demonstrated

The test suite exemplifies several testing best practices:

Comprehensive setup and teardown management
Systematic parameter variation testing
Precise numerical tolerance checks
Clear test case organization
Detailed documentation of test steps

coqui-ai/tts

tests/aux_tests/test_audio_processor.py

            
import os
import unittest

from tests import get_tests_input_path, get_tests_output_path, get_tests_path
from TTS.config import BaseAudioConfig
from TTS.utils.audio.processor import AudioProcessor

TESTS_PATH = get_tests_path()
OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests")
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")

os.makedirs(OUT_PATH, exist_ok=True)
conf = BaseAudioConfig(mel_fmax=8000, pitch_fmax=640, pitch_fmin=1)


# pylint: disable=protected-access
class TestAudio(unittest.TestCase):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.ap = AudioProcessor(**conf)

    def test_audio_synthesis(self):
        """1. load wav
        2. set normalization parameters
        3. extract mel-spec
        4. invert to wav and save the output
        """
        print(" > Sanity check for the process wav -> mel -> wav")

        def _test(max_norm, signal_norm, symmetric_norm, clip_norm):
            self.ap.max_norm = max_norm
            self.ap.signal_norm = signal_norm
            self.ap.symmetric_norm = symmetric_norm
            self.ap.clip_norm = clip_norm
            wav = self.ap.load_wav(WAV_FILE)
            mel = self.ap.melspectrogram(wav)
            wav_ = self.ap.inv_melspectrogram(mel)
            file_name = "/audio_test-melspec_max_norm_{}-signal_norm_{}-symmetric_{}-clip_norm_{}.wav".format(
                max_norm, signal_norm, symmetric_norm, clip_norm
            )
            print(" | > Creating wav file at : ", file_name)
            self.ap.save_wav(wav_, OUT_PATH + file_name)

        # maxnorm = 1.0
        _test(1.0, False, False, False)
        _test(1.0, True, False, False)
        _test(1.0, True, True, False)
        _test(1.0, True, False, True)
        _test(1.0, True, True, True)
        # maxnorm = 4.0
        _test(4.0, False, False, False)
        _test(4.0, True, False, False)
        _test(4.0, True, True, False)
        _test(4.0, True, False, True)
        _test(4.0, True, True, True)

    def test_normalize(self):
        """Check normalization and denormalization for range values and consistency"""
        print(" > Testing normalization and denormalization.")
        wav = self.ap.load_wav(WAV_FILE)
        wav = self.ap.sound_norm(wav)  # normalize audio to get abetter normalization range below.
        self.ap.signal_norm = False
        x = self.ap.melspectrogram(wav)
        x_old = x

        self.ap.signal_norm = True
        self.ap.symmetric_norm = False
        self.ap.clip_norm = False
        self.ap.max_norm = 4.0
        x_norm = self.ap.normalize(x)
        print(
            f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} --  {x_norm.min()}"
        )
        assert (x_old - x).sum() == 0
        # check value range
        assert x_norm.max() <= self.ap.max_norm + 1, x_norm.max()
        assert x_norm.min() >= 0 - 1, x_norm.min()
        # check denorm.
        x_ = self.ap.denormalize(x_norm)
        assert (x - x_).sum() < 1e-3, (x - x_).mean()

        self.ap.signal_norm = True
        self.ap.symmetric_norm = False
        self.ap.clip_norm = True
        self.ap.max_norm = 4.0
        x_norm = self.ap.normalize(x)
        print(
            f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} --  {x_norm.min()}"
        )

        assert (x_old - x).sum() == 0
        # check value range
        assert x_norm.max() <= self.ap.max_norm, x_norm.max()
        assert x_norm.min() >= 0, x_norm.min()
        # check denorm.
        x_ = self.ap.denormalize(x_norm)
        assert (x - x_).sum() < 1e-3, (x - x_).mean()

        self.ap.signal_norm = True
        self.ap.symmetric_norm = True
        self.ap.clip_norm = False
        self.ap.max_norm = 4.0
        x_norm = self.ap.normalize(x)
        print(
            f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} --  {x_norm.min()}"
        )

        assert (x_old - x).sum() == 0
        # check value range
        assert x_norm.max() <= self.ap.max_norm + 1, x_norm.max()
        assert x_norm.min() >= -self.ap.max_norm - 2, x_norm.min()  # pylint: disable=invalid-unary-operand-type
        assert x_norm.min() <= 0, x_norm.min()
        # check denorm.
        x_ = self.ap.denormalize(x_norm)
        assert (x - x_).sum() < 1e-3, (x - x_).mean()

        self.ap.signal_norm = True
        self.ap.symmetric_norm = True
        self.ap.clip_norm = True
        self.ap.max_norm = 4.0
        x_norm = self.ap.normalize(x)
        print(
            f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} --  {x_norm.min()}"
        )

        assert (x_old - x).sum() == 0
        # check value range
        assert x_norm.max() <= self.ap.max_norm, x_norm.max()
        assert x_norm.min() >= -self.ap.max_norm, x_norm.min()  # pylint: disable=invalid-unary-operand-type
        assert x_norm.min() <= 0, x_norm.min()
        # check denorm.
        x_ = self.ap.denormalize(x_norm)
        assert (x - x_).sum() < 1e-3, (x - x_).mean()

        self.ap.signal_norm = True
        self.ap.symmetric_norm = False
        self.ap.max_norm = 1.0
        x_norm = self.ap.normalize(x)
        print(
            f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} --  {x_norm.min()}"
        )

        assert (x_old - x).sum() == 0
        assert x_norm.max() <= self.ap.max_norm, x_norm.max()
        assert x_norm.min() >= 0, x_norm.min()
        x_ = self.ap.denormalize(x_norm)
        assert (x - x_).sum() < 1e-3

        self.ap.signal_norm = True
        self.ap.symmetric_norm = True
        self.ap.max_norm = 1.0
        x_norm = self.ap.normalize(x)
        print(
            f" > MaxNorm: {self.ap.max_norm}, ClipNorm:{self.ap.clip_norm}, SymmetricNorm:{self.ap.symmetric_norm}, SignalNorm:{self.ap.signal_norm} Range-> {x_norm.max()} --  {x_norm.min()}"
        )

        assert (x_old - x).sum() == 0
        assert x_norm.max() <= self.ap.max_norm, x_norm.max()
        assert x_norm.min() >= -self.ap.max_norm, x_norm.min()  # pylint: disable=invalid-unary-operand-type
        assert x_norm.min() < 0, x_norm.min()
        x_ = self.ap.denormalize(x_norm)
        assert (x - x_).sum() < 1e-3

    def test_scaler(self):
        scaler_stats_path = os.path.join(get_tests_input_path(), "scale_stats.npy")
        conf.stats_path = scaler_stats_path
        conf.preemphasis = 0.0
        conf.do_trim_silence = True
        conf.signal_norm = True

        ap = AudioProcessor(**conf)
        mel_mean, mel_std, linear_mean, linear_std, _ = ap.load_stats(scaler_stats_path)
        ap.setup_scaler(mel_mean, mel_std, linear_mean, linear_std)

        self.ap.signal_norm = False
        self.ap.preemphasis = 0.0

        # test scaler forward and backward transforms
        wav = self.ap.load_wav(WAV_FILE)
        mel_reference = self.ap.melspectrogram(wav)
        mel_norm = ap.melspectrogram(wav)
        mel_denorm = ap.denormalize(mel_norm)
        assert abs(mel_reference - mel_denorm).max() < 1e-4

    def test_compute_f0(self):  # pylint: disable=no-self-use
        ap = AudioProcessor(**conf)
        wav = ap.load_wav(WAV_FILE)
        pitch = ap.compute_f0(wav)
        mel = ap.melspectrogram(wav)
        assert pitch.shape[0] == mel.shape[1]