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speech to text to speech
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""" To use: install LLM studio (or Ollama), clone OpenVoice, run this script in the OpenVoice directory | |
git clone https://github.com/myshell-ai/OpenVoice | |
cd OpenVoice | |
git clone https://huggingface.co/myshell-ai/OpenVoice | |
cp -r OpenVoice/* . | |
pip install whisper pynput pyaudio | |
""" | |
from openai import OpenAI | |
import time | |
import pyaudio | |
import numpy as np | |
import torch | |
import os | |
import re | |
import se_extractor | |
import whisper | |
from pynput import keyboard | |
from api import BaseSpeakerTTS, ToneColorConverter | |
from utils import split_sentences_latin | |
SYSTEM_MESSAGE = "You are Bob an AI assistant. KEEP YOUR RESPONSES VERY SHORT AND CONVERSATIONAL." | |
SPEAKER_WAV = None | |
llm_client = OpenAI(base_url="http://localhost:1234/v1", api_key="not-needed") | |
tts_en_ckpt_base = os.path.join(os.path.dirname(__file__), "checkpoints/base_speakers/EN") | |
tts_ckpt_converter = os.path.join(os.path.dirname(__file__), "checkpoints/converter") | |
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" | |
tts_model = BaseSpeakerTTS(f'{tts_en_ckpt_base}/config.json', device=device) | |
tts_model.load_ckpt(f'{tts_en_ckpt_base}/checkpoint.pth') | |
tone_color_converter = ToneColorConverter(f'{tts_ckpt_converter}/config.json', device=device) | |
tone_color_converter.load_ckpt(f'{tts_ckpt_converter}/checkpoint.pth') | |
en_source_default_se = torch.load(f"{tts_en_ckpt_base}/en_default_se.pth").to(device) | |
target_se, _ = se_extractor.get_se(SPEAKER_WAV, tone_color_converter, target_dir='processed', vad=True) if SPEAKER_WAV else (None, None) | |
sampling_rate = tts_model.hps.data.sampling_rate | |
mark = tts_model.language_marks.get("english", None) | |
asr_model = whisper.load_model("base.en") | |
def play_audio(text): | |
p = pyaudio.PyAudio() | |
stream = p.open(format=pyaudio.paFloat32, channels=1, rate=sampling_rate, output=True) | |
texts = split_sentences_latin(text) | |
for t in texts: | |
audio_list = [] | |
t = re.sub(r'([a-z])([A-Z])', r'\1 \2', t) | |
t = f'[{mark}]{t}[{mark}]' | |
stn_tst = tts_model.get_text(t, tts_model.hps, False) | |
with torch.no_grad(): | |
x_tst = stn_tst.unsqueeze(0).to(tts_model.device) | |
x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).to(tts_model.device) | |
sid = torch.LongTensor([tts_model.hps.speakers["default"]]).to(tts_model.device) | |
audio = tts_model.model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=0.667, noise_scale_w=0.6)[0][0, 0].data.cpu().float().numpy() | |
if target_se is not None: | |
audio = tone_color_converter.convert_from_tensor(audio=audio, src_se=en_source_default_se, tgt_se=target_se) | |
audio_list.append(audio) | |
data = tts_model.audio_numpy_concat(audio_list, sr=sampling_rate).tobytes() | |
stream.write(data) | |
stream.stop_stream() | |
stream.close() | |
p.terminate() | |
def record_and_transcribe_audio(): | |
recording = False | |
def on_press(key): | |
nonlocal recording | |
if key == keyboard.Key.shift: | |
recording = True | |
def on_release(key): | |
nonlocal recording | |
if key == keyboard.Key.shift: | |
recording = False | |
return False | |
listener = keyboard.Listener( | |
on_press=on_press, | |
on_release=on_release) | |
listener.start() | |
print('Press shift to record...') | |
while not recording: | |
time.sleep(0.1) | |
print('Start recording...') | |
p = pyaudio.PyAudio() | |
stream = p.open(format=pyaudio.paInt16, channels=1, rate=16000, frames_per_buffer=1024, input=True) | |
frames = [] | |
while recording: | |
data = stream.read(1024, exception_on_overflow = False) | |
frames.append(np.frombuffer(data, dtype=np.int16)) | |
print('Finished recording') | |
data = np.hstack(frames, dtype=np.float32) / 32768.0 | |
result = asr_model.transcribe(data)['text'] | |
stream.stop_stream() | |
stream.close() | |
p.terminate() | |
return result | |
def conversation(): | |
conversation_history = [{'role': 'system', 'content': SYSTEM_MESSAGE}] | |
while True: | |
user_input = record_and_transcribe_audio() | |
conversation_history.append({'role': 'user', 'content': user_input}) | |
response = llm_client.chat.completions.create(model="local-model", messages=conversation_history) | |
chatbot_response = response.choices[0].message.content | |
conversation_history.append({'role': 'assistant', 'content': chatbot_response}) | |
print(conversation_history) | |
play_audio(chatbot_response) | |
if len(conversation_history) > 20: | |
conversation_history = conversation_history[-20:] | |
conversation() |
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Yes it does on my system too. Would make changes to it , integrate speech brain probably