|
|
|
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) |
|
|
|
speaker_files = glob.glob(local_download_dir + "/speaker*") |
|
language_files = glob.glob(local_download_dir + "/language*") |
|
language_id = "" |
|
if len(speaker_files) > 0: |
|
|
|
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]) |
|
|
|
|
|
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: |
|
|
|
run_cli( |
|
f"tts --model_name {model_name} " |
|
f'--text "This is an example." --out_path "{output_path}" --progress_bar False' |
|
) |
|
|
|
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: |
|
|
|
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 |
|
normal_len = sum([len(chunk) for chunk in wav_chuncks]) |
|
|
|
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, |
|
speed=1.5, |
|
) |
|
wav_chuncks = [] |
|
for i, chunk in enumerate(chunks): |
|
wav_chuncks.append(chunk) |
|
fast_len = sum([len(chunk) for chunk in wav_chuncks]) |
|
|
|
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, |
|
speed=0.66, |
|
) |
|
wav_chuncks = [] |
|
for i, chunk in enumerate(chunks): |
|
wav_chuncks.append(chunk) |
|
slow_len = sum([len(chunk) for chunk in wav_chuncks]) |
|
|
|
assert slow_len > normal_len |
|
assert normal_len > fast_len |
|
|
|
|
|
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) |
|
|
