Back to Repositories

Testing Speaker Encoder Training Workflow in Coqui-AI TTS

This test suite validates the training functionality of the speaker encoder component in the Coqui-AI TTS system. It covers model initialization, training cycles, and different loss functions for speaker embedding generation.

Test Coverage Overview

The test suite provides comprehensive coverage of speaker encoder training scenarios:

Initial model training and continuation
Testing different model architectures (default and ResNet)
Validation of multiple loss functions (ge2e, softmaxproto)
Configuration persistence and loading

Implementation Analysis

The testing approach implements a systematic validation of the speaker encoder training pipeline. It utilizes CLI command execution patterns to simulate real training scenarios, with specific focus on model continuation and configuration management.

The implementation leverages Python’s path handling and shell command execution capabilities for comprehensive testing.

Technical Details

Key technical components include:

SpeakerEncoderConfig for model configuration
BaseAudioConfig for audio processing settings
CUDA device management for GPU training
File system operations for model checkpoints
Dynamic path handling for training outputs

Best Practices Demonstrated

The test suite exemplifies several testing best practices:

Isolated test environment with controlled configurations
Proper resource cleanup after test execution
Modular test structure with clear separation of concerns
Comprehensive validation of different model architectures and loss functions

coqui-ai/tts

tests/aux_tests/test_speaker_encoder_train.py

            
import glob
import os
import shutil

from tests import get_device_id, get_tests_output_path, run_cli
from TTS.config.shared_configs import BaseAudioConfig
from TTS.encoder.configs.speaker_encoder_config import SpeakerEncoderConfig


def run_test_train():
    command = (
        f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --config_path {config_path} "
        f"--coqpit.output_path {output_path} "
        "--coqpit.datasets.0.formatter ljspeech_test "
        "--coqpit.datasets.0.meta_file_train metadata.csv "
        "--coqpit.datasets.0.meta_file_val metadata.csv "
        "--coqpit.datasets.0.path tests/data/ljspeech "
    )
    run_cli(command)


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

config = SpeakerEncoderConfig(
    batch_size=4,
    num_classes_in_batch=4,
    num_utter_per_class=2,
    eval_num_classes_in_batch=4,
    eval_num_utter_per_class=2,
    num_loader_workers=1,
    epochs=1,
    print_step=1,
    save_step=2,
    print_eval=True,
    run_eval=True,
    audio=BaseAudioConfig(num_mels=80),
)
config.audio.do_trim_silence = True
config.audio.trim_db = 60
config.loss = "ge2e"
config.save_json(config_path)

print(config)
# train the model for one epoch
run_test_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_encoder.py --continue_path {continue_path} "
)
run_cli(command_train)
shutil.rmtree(continue_path)

# test resnet speaker encoder
config.model_params["model_name"] = "resnet"
config.save_json(config_path)

# train the model for one epoch
run_test_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_encoder.py --continue_path {continue_path} "
)
run_cli(command_train)
shutil.rmtree(continue_path)

# test model with ge2e loss function
# config.loss = "ge2e"
# config.save_json(config_path)
# run_test_train()

# test model with angleproto loss function
# config.loss = "angleproto"
# config.save_json(config_path)
# run_test_train()

# test model with softmaxproto loss function
config.loss = "softmaxproto"
config.save_json(config_path)
run_test_train()