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Testing AlignTTS Training and Inference Implementation in Coqui-AI TTS

This test suite validates the training and inference functionality of AlignTTS model implementation in the Coqui-AI TTS framework. It covers model configuration, training initialization, checkpoint management, and inference capabilities.

Test Coverage Overview

The test suite provides comprehensive coverage of AlignTTS model training and inference workflows.

Key areas tested include:

Model configuration setup and validation
Training initialization and execution
Checkpoint management and restoration
Inference pipeline verification
Config file integrity checks

Implementation Analysis

The testing approach implements an end-to-end validation of the AlignTTS pipeline using a systematic workflow.

Key implementation patterns include:

Dynamic device configuration for CUDA
CLI-based training and inference execution
Checkpoint detection and management
Configuration persistence and validation

Technical Details

Testing tools and configuration:

Python unit testing framework
CUDA device management
LJSpeech dataset formatter
JSON configuration management
CLI command execution wrapper
Temporary output path handling

Best Practices Demonstrated

The test implementation showcases several testing best practices:

Isolated test environment setup
Comprehensive configuration validation
Resource cleanup after test execution
Clear separation of setup, execution, and validation steps
Proper error handling and resource management

coqui-ai/tts

tests/tts_tests2/test_align_tts_train.py

            
import glob
import json
import os
import shutil

from trainer import get_last_checkpoint

from tests import get_device_id, get_tests_output_path, run_cli
from TTS.tts.configs.align_tts_config import AlignTTSConfig

config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
output_path = os.path.join(get_tests_output_path(), "train_outputs")


config = AlignTTSConfig(
    batch_size=8,
    eval_batch_size=8,
    num_loader_workers=0,
    num_eval_loader_workers=0,
    text_cleaner="english_cleaners",
    use_phonemes=False,
    phoneme_language="en-us",
    phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"),
    run_eval=True,
    test_delay_epochs=-1,
    epochs=1,
    print_step=1,
    print_eval=True,
    test_sentences=[
        "Be a voice, not an echo.",
    ],
)

config.audio.do_trim_silence = True
config.audio.trim_db = 60
config.save_json(config_path)

# train the model for one epoch
command_train = (
    f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
    f"--coqpit.output_path {output_path} "
    "--coqpit.datasets.0.formatter ljspeech "
    "--coqpit.datasets.0.meta_file_train metadata.csv "
    "--coqpit.datasets.0.meta_file_val metadata.csv "
    "--coqpit.datasets.0.path tests/data/ljspeech "
    "--coqpit.test_delay_epochs 0 "
)
run_cli(command_train)

# Find latest folder
continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)

# Inference using TTS API
continue_config_path = os.path.join(continue_path, "config.json")
continue_restore_path, _ = get_last_checkpoint(continue_path)
out_wav_path = os.path.join(get_tests_output_path(), "output.wav")

# Check integrity of the config
with open(continue_config_path, "r", encoding="utf-8") as f:
    config_loaded = json.load(f)
assert config_loaded["characters"] is not None
assert config_loaded["output_path"] in continue_path
assert config_loaded["test_delay_epochs"] == 0

# Load the model and run inference
inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
run_cli(inference_command)

# restore the model and continue training for one more epoch
command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
run_cli(command_train)
shutil.rmtree(continue_path)