mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-05 22:20:01 +08:00
102 lines
4.2 KiB
Python
102 lines
4.2 KiB
Python
import torch
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import sounddevice as sd
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import time
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from queue import Queue
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from threading import Thread
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import os
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class TTS:
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def __init__(self):
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# Replace with your checkpoints and reference audio here
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# Note: Using a venv may require updating the default paths provided here
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self.bert_checkpoint = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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self.cnhuhbert_checkpoint = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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# self.t2s_checkpoint = "GPT_SoVITS/pretrained_models/ayaka/Ayaka-e15.ckpt"
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# self.vits_checkpoint = "GPT_SoVITS/pretrained_models/ayaka/Ayaka_e3_s1848_l32.pth"
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self.t2s_checkpoint = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt"
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self.vits_checkpoint = "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth"
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self.ref_audio = "audio/ayaka/ref_audio/10_audio.wav"
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from GPT_SoVITS.TTS_infer_pack.TTS import TTS, TTS_Config
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self.config = {
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"custom": {
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"bert_base_path": self.bert_checkpoint,
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"cnhuhbert_base_path": self.cnhuhbert_checkpoint,
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"device": "cuda" if torch.cuda.is_available() else "cpu",
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"is_half": True,
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"t2s_weights_path": self.t2s_checkpoint,
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"vits_weights_path": self.vits_checkpoint,
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"version": "v3"
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}
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}
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self.tts = TTS(TTS_Config(self.config))
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self.audio_queue = Queue()
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self.generating_audio = False
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def audio_stream(self, start_time):
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with sd.OutputStream(samplerate=32000, channels=1, dtype="int16") as stream:
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while True:
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sr, audio_data = self.audio_queue.get()
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if audio_data is None:
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print(f"Stream Thread Done ({time.time() - start_time:.2f}s)")
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break
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print((sr, audio_data))
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stream.write(audio_data)
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self.generating_audio = False
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def synthesize(self, text, start_time, generating_text=False):
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if not self.generating_audio:
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Thread(target=self.audio_stream, args=(start_time,)).start()
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self.generating_audio = True
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path = "audio/ayaka/aux_ref_audio"
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aux_ref_audios = [f"{path}/{file_name}" for file_name in os.listdir(path)]
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args = {
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"text": text,
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"text_lang": "en",
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"ref_audio_path": self.ref_audio,
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"aux_ref_audio_paths": aux_ref_audios,
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"prompt_text": "Don't worry. Now that I've experienced the event once already, I won't be easily frightened. I'll see you later. Have a lovely chat with your friend.",
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"prompt_lang": "en",
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"temperature": 0.8,
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"top_k": 50,
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"top_p": 0.9,
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"parallel_infer": True,
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"sample_steps": 32,
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"super_sampling": True,
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"speed_factor": 1,
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"fragment_interval": 0.2
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# "stream_output": True,
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# "max_chunk_size": 20,
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}
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if text:
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print(f"Synthesis Start: {time.time() - start_time}")
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generator = self.tts.run(args)
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while True:
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try:
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audio_chunk = next(generator)
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self.audio_queue.put(audio_chunk)
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except StopIteration:
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break
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if not generating_text:
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self.audio_queue.put((None, None))
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print(f"Synthesis End ({time.time() - start_time:.2f}s)")
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# Usage
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tts = TTS()
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"""
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Time is only for debugging purposes. If not needed, feel free to remove.
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Since this TTS model was built to be paired with LLM text streaming, we use a generating_text bool
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this bool signifies if we are receiving the last chunk of streamed text (hence if we are generating anymore).
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"""
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tts.synthesize("One day, a fierce storm rolled in, bringing heavy rain and strong winds that threatened to destroy the wheat crops.", time.time(), False)
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while tts.generating_audio:
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time.sleep(0.1)
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tts.synthesize("One day, a fierce storm rolled in, bringing heavy rain and strong winds that threatened to destroy the wheat crops.", time.time(), False) |