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Testing MelGAN Vocoder Training Implementation in Coqui-TTS

This test suite validates the training functionality of the MelGAN vocoder model in the Coqui-TTS framework. It focuses on testing model configuration, training initialization, and training continuation capabilities.

Test Coverage Overview

The test suite covers essential aspects of MelGAN vocoder training:

Configuration setup and validation
Initial training execution
Model checkpoint management
Training continuation from saved checkpoints
Resource cleanup post-training

Implementation Analysis

The testing approach implements a systematic validation of the MelGAN training pipeline. It utilizes a minimal configuration for quick testing, with specific batch sizes, worker counts, and sequence lengths. The test verifies both fresh training initialization and continuation from saved checkpoints.

Key patterns include CUDA device management, CLI command execution, and filesystem operations for managing training artifacts.

Technical Details

Testing infrastructure includes:

Python’s built-in testing utilities
Custom CLI runners for training scripts
CUDA device management
File system operations (glob, shutil)
MelGAN-specific configuration parameters
LJSpeech dataset for training validation

Best Practices Demonstrated

The test implementation showcases several testing best practices:

Isolated test environment with controlled configurations
Complete training cycle validation
Proper resource cleanup
Deterministic device selection
Minimal but sufficient training parameters
Efficient test execution with small epoch counts

coqui-ai/tts

tests/vocoder_tests/test_melgan_train.py

            
import glob
import os
import shutil

from tests import get_device_id, get_tests_output_path, run_cli
from TTS.vocoder.configs import MelganConfig

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

config = MelganConfig(
    batch_size=4,
    eval_batch_size=4,
    num_loader_workers=0,
    num_eval_loader_workers=0,
    run_eval=True,
    test_delay_epochs=-1,
    epochs=1,
    seq_len=2048,
    eval_split_size=1,
    print_step=1,
    discriminator_model_params={"base_channels": 16, "max_channels": 64, "downsample_factors": [4, 4, 4]},
    print_eval=True,
    data_path="tests/data/ljspeech",
    output_path=output_path,
)
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_vocoder.py --config_path {config_path} "
run_cli(command_train)

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

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