mirror of
https://github.com/coqui-ai/TTS.git
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f81892483d
* Rename Speaker encoder module to encoder * Add a generic emotion dataset formatter * Transform the Speaker Encoder dataset to a generic dataset and create emotion encoder config * Add class map in emotion config * Add Base encoder config * Add evaluation encoder script * Fix the bug in plot_embeddings * Enable Weight decay for encoder training * Add argumnet to disable storage * Add Perfect Sampler and remove storage * Add evaluation during encoder training * Fix lint checks * Remove useless config parameter * Active evaluation in speaker encoder test and use multispeaker dataset for this test * Unit tests fixs * Remove useless tests for speedup the aux_tests * Use get_optimizer in Encoder * Add BaseEncoder Class * Fix the unitests * Add Perfect Batch Sampler unit test * Add compute encoder accuracy in a function
89 lines
2.5 KiB
Python
89 lines
2.5 KiB
Python
import glob
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import os
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import shutil
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from tests import get_device_id, get_tests_output_path, run_cli
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from TTS.config.shared_configs import BaseAudioConfig
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from TTS.encoder.configs.speaker_encoder_config import SpeakerEncoderConfig
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def run_test_train():
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command = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --config_path {config_path} "
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f"--coqpit.output_path {output_path} "
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"--coqpit.datasets.0.name ljspeech_test "
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"--coqpit.datasets.0.meta_file_train metadata.csv "
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"--coqpit.datasets.0.meta_file_val metadata.csv "
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"--coqpit.datasets.0.path tests/data/ljspeech "
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)
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run_cli(command)
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config_path = os.path.join(get_tests_output_path(), "test_speaker_encoder_config.json")
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output_path = os.path.join(get_tests_output_path(), "train_outputs")
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config = SpeakerEncoderConfig(
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batch_size=4,
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num_classes_in_batch=4,
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num_utter_per_class=2,
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eval_num_classes_in_batch=4,
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eval_num_utter_per_class=2,
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num_loader_workers=1,
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epochs=1,
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print_step=1,
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save_step=2,
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print_eval=True,
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run_eval=True,
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audio=BaseAudioConfig(num_mels=80),
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)
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config.audio.do_trim_silence = True
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config.audio.trim_db = 60
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config.loss = "ge2e"
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config.save_json(config_path)
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print(config)
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# train the model for one epoch
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run_test_train()
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# Find latest folder
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continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
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# restore the model and continue training for one more epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --continue_path {continue_path} "
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)
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run_cli(command_train)
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shutil.rmtree(continue_path)
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# test resnet speaker encoder
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config.model_params["model_name"] = "resnet"
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config.save_json(config_path)
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# train the model for one epoch
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run_test_train()
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# Find latest folder
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continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
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# restore the model and continue training for one more epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --continue_path {continue_path} "
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)
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run_cli(command_train)
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shutil.rmtree(continue_path)
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# test model with ge2e loss function
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# config.loss = "ge2e"
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# config.save_json(config_path)
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# run_test_train()
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# test model with angleproto loss function
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# config.loss = "angleproto"
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# config.save_json(config_path)
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# run_test_train()
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# test model with softmaxproto loss function
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config.loss = "softmaxproto"
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config.save_json(config_path)
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run_test_train()
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