#!/usr/bin/env python3` import glob import os import shutil import torch from tests import get_tests_data_path, get_tests_output_path, run_cli from TTS.tts.utils.languages import LanguageManager from TTS.tts.utils.speakers import SpeakerManager from TTS.utils.generic_utils import get_user_data_dir from TTS.utils.manage import ModelManager MODELS_WITH_SEP_TESTS = [ "tts_models/multilingual/multi-dataset/bark", "tts_models/en/multi-dataset/tortoise-v2", "tts_models/multilingual/multi-dataset/xtts_v1.1", "tts_models/multilingual/multi-dataset/xtts_v2", ] def run_models(offset=0, step=1): """Check if all the models are downloadable and tts models run correctly.""" print(" > Run synthesizer with all the models.") output_path = os.path.join(get_tests_output_path(), "output.wav") manager = ModelManager(output_prefix=get_tests_output_path(), progress_bar=False) model_names = [name for name in manager.list_models() if name not in MODELS_WITH_SEP_TESTS] print("Model names:", model_names) for model_name in model_names[offset::step]: print(f"\n > Run - {model_name}") model_path, _, _ = manager.download_model(model_name) if "tts_models" in model_name: local_download_dir = os.path.dirname(model_path) # download and run the model speaker_files = glob.glob(local_download_dir + "/speaker*") language_files = glob.glob(local_download_dir + "/language*") language_id = "" if len(speaker_files) > 0: # multi-speaker model if "speaker_ids" in speaker_files[0]: speaker_manager = SpeakerManager(speaker_id_file_path=speaker_files[0]) elif "speakers" in speaker_files[0]: speaker_manager = SpeakerManager(d_vectors_file_path=speaker_files[0]) # multi-lingual model - Assuming multi-lingual models are also multi-speaker if len(language_files) > 0 and "language_ids" in language_files[0]: language_manager = LanguageManager(language_ids_file_path=language_files[0]) language_id = language_manager.language_names[0] speaker_id = list(speaker_manager.name_to_id.keys())[0] run_cli( f"tts --model_name {model_name} " f'--text "This is an example." --out_path "{output_path}" --speaker_idx "{speaker_id}" --language_idx "{language_id}" --progress_bar False' ) else: # single-speaker model run_cli( f"tts --model_name {model_name} " f'--text "This is an example." --out_path "{output_path}" --progress_bar False' ) # remove downloaded models shutil.rmtree(local_download_dir) shutil.rmtree(get_user_data_dir("tts")) elif "voice_conversion_models" in model_name: speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") reference_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0032.wav") run_cli( f"tts --model_name {model_name} " f'--out_path "{output_path}" --source_wav "{speaker_wav}" --target_wav "{reference_wav}" --progress_bar False' ) else: # only download the model manager.download_model(model_name) print(f" | > OK: {model_name}") def test_xtts(): """XTTS is too big to run on github actions. We need to test it locally""" output_path = os.path.join(get_tests_output_path(), "output.wav") speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") use_gpu = torch.cuda.is_available() if use_gpu: run_cli( "yes | " f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v1.1 " f'--text "This is an example." --out_path "{output_path}" --progress_bar False --use_cuda True ' f'--speaker_wav "{speaker_wav}" --language_idx "en"' ) else: run_cli( "yes | " f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v1.1 " f'--text "This is an example." --out_path "{output_path}" --progress_bar False ' f'--speaker_wav "{speaker_wav}" --language_idx "en"' ) def test_xtts_streaming(): """Testing the new inference_stream method""" from TTS.tts.configs.xtts_config import XttsConfig from TTS.tts.models.xtts import Xtts speaker_wav = [os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")] speaker_wav_2 = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0002.wav") speaker_wav.append(speaker_wav_2) model_path = os.path.join(get_user_data_dir("tts"), "tts_models--multilingual--multi-dataset--xtts_v1.1") config = XttsConfig() config.load_json(os.path.join(model_path, "config.json")) model = Xtts.init_from_config(config) model.load_checkpoint(config, checkpoint_dir=model_path) model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) print("Computing speaker latents...") gpt_cond_latent, _, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav) print("Inference...") chunks = model.inference_stream( "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", "en", gpt_cond_latent, speaker_embedding, ) wav_chuncks = [] for i, chunk in enumerate(chunks): if i == 0: assert chunk.shape[-1] > 5000 wav_chuncks.append(chunk) assert len(wav_chuncks) > 1 def test_xtts_v2(): """XTTS is too big to run on github actions. We need to test it locally""" output_path = os.path.join(get_tests_output_path(), "output.wav") speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") speaker_wav_2 = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0002.wav") use_gpu = torch.cuda.is_available() if use_gpu: run_cli( "yes | " f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 " f'--text "This is an example." --out_path "{output_path}" --progress_bar False --use_cuda True ' f'--speaker_wav "{speaker_wav}" "{speaker_wav_2}" "--language_idx "en"' ) else: run_cli( "yes | " f"tts --model_name tts_models/multilingual/multi-dataset/xtts_v2 " f'--text "This is an example." --out_path "{output_path}" --progress_bar False ' f'--speaker_wav "{speaker_wav}" "{speaker_wav_2}" --language_idx "en"' ) def test_xtts_v2_streaming(): """Testing the new inference_stream method""" from TTS.tts.configs.xtts_config import XttsConfig from TTS.tts.models.xtts import Xtts speaker_wav = [os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav")] model_path = os.path.join(get_user_data_dir("tts"), "tts_models--multilingual--multi-dataset--xtts_v2") config = XttsConfig() config.load_json(os.path.join(model_path, "config.json")) model = Xtts.init_from_config(config) model.load_checkpoint(config, checkpoint_dir=model_path) model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) print("Computing speaker latents...") gpt_cond_latent, _, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav) print("Inference...") chunks = model.inference_stream( "It took me quite a long time to develop a voice and now that I have it I am not going to be silent.", "en", gpt_cond_latent, speaker_embedding, ) wav_chuncks = [] for i, chunk in enumerate(chunks): if i == 0: assert chunk.shape[-1] > 5000 wav_chuncks.append(chunk) assert len(wav_chuncks) > 1 def test_tortoise(): output_path = os.path.join(get_tests_output_path(), "output.wav") use_gpu = torch.cuda.is_available() if use_gpu: run_cli( f" tts --model_name tts_models/en/multi-dataset/tortoise-v2 " f'--text "This is an example." --out_path "{output_path}" --progress_bar False --use_cuda True' ) else: run_cli( f" tts --model_name tts_models/en/multi-dataset/tortoise-v2 " f'--text "This is an example." --out_path "{output_path}" --progress_bar False' ) def test_bark(): """Bark is too big to run on github actions. We need to test it locally""" output_path = os.path.join(get_tests_output_path(), "output.wav") use_gpu = torch.cuda.is_available() if use_gpu: run_cli( f" tts --model_name tts_models/multilingual/multi-dataset/bark " f'--text "This is an example." --out_path "{output_path}" --progress_bar False --use_cuda True' ) else: run_cli( f" tts --model_name tts_models/multilingual/multi-dataset/bark " f'--text "This is an example." --out_path "{output_path}" --progress_bar False' ) def test_voice_conversion(): print(" > Run voice conversion inference using YourTTS model.") model_name = "tts_models/multilingual/multi-dataset/your_tts" language_id = "en" speaker_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0001.wav") reference_wav = os.path.join(get_tests_data_path(), "ljspeech", "wavs", "LJ001-0032.wav") output_path = os.path.join(get_tests_output_path(), "output.wav") run_cli( f"tts --model_name {model_name}" f" --out_path {output_path} --speaker_wav {speaker_wav} --reference_wav {reference_wav} --language_idx {language_id} --progress_bar False" ) """ These are used to split tests into different actions on Github. """ def test_models_offset_0_step_3(): run_models(offset=0, step=3) def test_models_offset_1_step_3(): run_models(offset=1, step=3) def test_models_offset_2_step_3(): run_models(offset=2, step=3)