#!/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
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)