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import os
import time
import re
import wave
import pyaudio
import subprocess
import numpy as np
from funasr import AutoModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
bat_file_path = 'GPT-SoVITS-v2-240821\\go-cli.bat'
model_name = "model/Qwen2.5-7B-Instruct"
print("初始化中...")
with open('background.txt', 'r', encoding='utf-8') as file:
background = file.read()
with open('STT-background.txt', 'r', encoding='utf-8') as file:
stt_background = file.read()
def extract_language(text):
text = re.sub(r'（[^）]*）', '', text)
text = re.sub(r'【[^】]*】', '', text)
return text
def play_wav(file_path):
with wave.open(file_path, 'rb') as wf:
p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),
channels=wf.getnchannels(),
rate=wf.getframerate(),
output=True)
data = wf.readframes(1024)
while data:
stream.write(data)
data = wf.readframes(1024)
stream.stop_stream()
stream.close()
p.terminate()
# 使用 4 位量化配置
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype="float16",
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=quantization_config,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
subprocess.run([bat_file_path], shell=True)
def correct(sentence):
messages = [
{"role": "system", "content": stt_background},
{"role": "user", "content": sentence}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response
model_dir = "model"
stt_model = AutoModel(
model=f"{model_dir}/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
vad_model=f"{model_dir}/speech_fsmn_vad_zh-cn-16k-common-pytorch",
punc_model=f"{model_dir}/punc_ct-transformer_cn-en-common-vocab471067-large",
disable_update=True
)
def stt():
chunk_size = 16000 * 3 # 3s
#chunk_stride = chunk_size # 确保每块长度足够
# 初始化麦克风输入
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16,
channels=1,
rate=16000,
input=True,
frames_per_buffer=chunk_size)
cache = {}
result_text = ""
sound_threshold = 500
wait_time = 1
no_sound_start_time = time.time()
try:
while True:
audio_data = stream.read(chunk_size)
speech_chunk = np.frombuffer(audio_data, dtype=np.int16)
if np.max(speech_chunk) > sound_threshold:
# 保存音频块为临时文件
temp_wav_path = "temp_chunk.wav"
with wave.open(temp_wav_path, 'wb') as wf:
wf.setnchannels(1)
wf.setsampwidth(p.get_sample_size(pyaudio.paInt16))
wf.setframerate(16000)
wf.writeframes(speech_chunk.tobytes())
res = stt_model.generate(input=temp_wav_path, cache=cache, is_final=False, chunk_size=chunk_size)
os.remove(temp_wav_path)
#print(f"Model output: {res}")
if res and len(res[0]["text"]) > 0:
result_text += res[0]["text"]
#corrected_text = correct(sentence=result_text)
print("未修改：", result_text)
#print("Qwen2.5修改：", corrected_text)
no_sound_start_time = time.time()
else:
if len(result_text) > 0 and time.time() - no_sound_start_time > wait_time:
print("已停顿，开始修正")
print("Qwen2.5修正：",correct(result_text))
no_sound_start_time = time.time()
return correct(result_text)
finally:
stream.stop_stream()
stream.close()
p.terminate()
print("初始化完成！")
while True:
prompt = stt()
#if prompt == '退出':
# break
messages = [
{"role": "system", "content": background},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
target_text = extract_language(response)
with open('GPT-SoVITS-v2-240821/target_text.txt', 'w', encoding='utf-8') as file:
file.write(target_text)
subprocess.run([bat_file_path], shell=True)
print("流萤猫酱:",response)
play_wav("GPT-SoVITS-v2-240821/output/output.wav")
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