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import os | |
import uuid | |
import numpy as np | |
import torch | |
import soundfile as sf | |
from gtts import gTTS | |
import edge_tts | |
from inference import Inference | |
import asyncio | |
#git+https://github.com/suno-ai/bark.git | |
# from transformers import AutoProcessor, BarkModel | |
# import nltk | |
# from nltk.tokenize import sent_tokenize | |
# from bark import SAMPLE_RATE | |
# now_dir = os.getcwd() | |
def cast_to_device(tensor, device): | |
try: | |
return tensor.to(device) | |
except Exception as e: | |
print(e) | |
return tensor | |
# Buscar la forma de evitar descargar el archivo de 4gb cada vez que crea una instancia | |
# def _bark_conversion_(text, voice_preset): | |
# os.makedirs(os.path.join(now_dir, "tts"), exist_ok=True) | |
# device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
# dtype = torch.float32 if "cpu" in device else torch.float16 | |
# bark_processor = AutoProcessor.from_pretrained( | |
# "suno/bark", | |
# cache_dir=os.path.join(now_dir, "tts", "suno/bark"), | |
# torch_dtype=dtype, | |
# ) | |
# bark_model = BarkModel.from_pretrained( | |
# "suno/bark", | |
# cache_dir=os.path.join(now_dir, "tts", "suno/bark"), | |
# torch_dtype=dtype, | |
# ).to(device) | |
# # bark_model.enable_cpu_offload() | |
# inputs = bark_processor(text=[text], return_tensors="pt", voice_preset=voice_preset) | |
# tensor_dict = { | |
# k: cast_to_device(v, device) if hasattr(v, "to") else v | |
# for k, v in inputs.items() | |
# } | |
# speech_values = bark_model.generate(**tensor_dict, do_sample=True) | |
# sampling_rate = bark_model.generation_config.sample_rate | |
# speech = speech_values.cpu().numpy().squeeze() | |
# return speech, sampling_rate | |
def tts_infer(tts_text, model_url, tts_method, tts_model): | |
print("*****************") | |
print(tts_text) | |
print(model_url) | |
if not tts_text: | |
return 'Primero escribe el texto que quieres convertir.', None | |
if not tts_model: | |
return 'Selecciona un modelo TTS antes de convertir.', None | |
if not model_url: | |
return 'Escribe la url de modelo que quieres usar antes de convertir.', None | |
f0_method = "harvest" | |
output_folder = "audios" | |
os.makedirs(output_folder, exist_ok=True) | |
converted_tts_filename = os.path.join(output_folder, f"tts_out_{uuid.uuid4()}.wav") | |
success = False | |
if len(tts_text) > 60: | |
tts_text = tts_text[:60] | |
print("DEMO; limit to 60 characters") | |
language = tts_model[:2] | |
if tts_method == "Edge-tts": | |
try: | |
asyncio.run( | |
edge_tts.Communicate( | |
tts_text, "-".join(tts_model.split("-")[:-1]) | |
).save(converted_tts_filename) | |
) | |
success = True | |
except Exception as e: | |
print("ERROR", e) | |
try: | |
tts = gTTS(tts_text, lang=language) | |
tts.save(converted_tts_filename) | |
print( | |
f"No audio was received. Please change the tts voice for {tts_model}. USING gTTS." | |
) | |
success = True | |
except: | |
tts = gTTS("a", lang=language) | |
tts.save(converted_tts_filename) | |
print("Error: Audio will be replaced.") | |
success = False | |
# elif tts_method == "Bark-tts": | |
# try: | |
# script = tts_text.replace("\n", " ").strip() | |
# sentences = sent_tokenize(script) | |
# silence = np.zeros(int(0.25 * SAMPLE_RATE)) | |
# pieces = [] | |
# for sentence in sentences: | |
# audio_array, _ = _bark_conversion_(sentence, tts_model.split("-")[0]) | |
# pieces += [audio_array, silence.copy()] | |
# sf.write( | |
# file=converted_tts_filename, samplerate=SAMPLE_RATE, data=np.concatenate(pieces) | |
# ) | |
# except Exception as e: | |
# print(f"{e}") | |
# return None, None | |
if success: | |
inference = Inference( | |
model_name=model_url, | |
f0_method=f0_method, | |
source_audio_path=converted_tts_filename, | |
output_file_name=os.path.join("./audio-outputs", os.path.basename(converted_tts_filename)), | |
) | |
output = inference.run() | |
if os.path.exists(converted_tts_filename): | |
os.remove(converted_tts_filename) | |
if os.path.exists(os.path.join("weights", inference.model_name)): | |
os.remove(os.path.join("weights", inference.model_name)) | |
if 'success' in output and output['success']: | |
return output, output['file'] | |
else: | |
return output, None | |
else: | |
return "Ocurri贸 un error durante la conversi贸n", None | |