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Testing TTS Spectrogram Extraction Implementation in Coqui-AI/TTS

This test suite validates the spectrogram extraction functionality for various TTS models in the Coqui-AI TTS framework. It ensures proper handling of different model architectures including GlowTTS, Tacotron2, and Tacotron, verifying their ability to process and extract spectrograms from checkpoints.

Test Coverage Overview

The test suite provides comprehensive coverage of spectrogram extraction across multiple TTS model architectures.

Tests GlowTTS model spectrogram extraction
Validates Tacotron2 spectrogram processing
Verifies original Tacotron model functionality
Covers checkpoint loading and processing

Implementation Analysis

The implementation follows a consistent pattern for each model test, utilizing PyTorch for model handling and CLI commands for extraction.

Each test case follows a structured approach:

Configuration loading and model setup
Checkpoint creation and saving
CLI-based extraction execution
Cleanup of temporary files

Technical Details

Uses unittest framework for test organization
Implements PyTorch for model operations
Leverages custom CLI runner for extraction commands
Manages test paths through helper functions
Disables CUDA for consistent CPU-based testing

Best Practices Demonstrated

The test suite demonstrates several testing best practices for machine learning applications.

Consistent test structure across different models
Proper resource cleanup after test execution
Static method usage for standalone tests
Clear separation of configuration and execution steps
Deterministic testing with fixed random seed

coqui-ai/tts

tests/aux_tests/test_extract_tts_spectrograms.py

            
import os
import unittest

import torch

from tests import get_tests_input_path, get_tests_output_path, run_cli
from TTS.config import load_config
from TTS.tts.models import setup_model

torch.manual_seed(1)


# pylint: disable=protected-access
class TestExtractTTSSpectrograms(unittest.TestCase):
    @staticmethod
    def test_GlowTTS():
        # set paths
        config_path = os.path.join(get_tests_input_path(), "test_glow_tts.json")
        checkpoint_path = os.path.join(get_tests_output_path(), "glowtts.pth")
        output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
        # load config
        c = load_config(config_path)
        # create model
        model = setup_model(c)
        # save model
        torch.save({"model": model.state_dict()}, checkpoint_path)
        # run test
        run_cli(
            f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"'
        )
        run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')

    @staticmethod
    def test_Tacotron2():
        # set paths
        config_path = os.path.join(get_tests_input_path(), "test_tacotron2_config.json")
        checkpoint_path = os.path.join(get_tests_output_path(), "tacotron2.pth")
        output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
        # load config
        c = load_config(config_path)
        # create model
        model = setup_model(c)
        # save model
        torch.save({"model": model.state_dict()}, checkpoint_path)
        # run test
        run_cli(
            f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"'
        )
        run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')

    @staticmethod
    def test_Tacotron():
        # set paths
        config_path = os.path.join(get_tests_input_path(), "test_tacotron_config.json")
        checkpoint_path = os.path.join(get_tests_output_path(), "tacotron.pth")
        output_path = os.path.join(get_tests_output_path(), "output_extract_tts_spectrograms/")
        # load config
        c = load_config(config_path)
        # create model
        model = setup_model(c)
        # save model
        torch.save({"model": model.state_dict()}, checkpoint_path)
        # run test
        run_cli(
            f'CUDA_VISIBLE_DEVICES="" python TTS/bin/extract_tts_spectrograms.py --config_path "{config_path}" --checkpoint_path "{checkpoint_path}" --output_path "{output_path}"'
        )
        run_cli(f'rm -rf "{output_path}" "{checkpoint_path}"')