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@thomwolf
Last active October 7, 2024 05:13
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speech to text to speech
""" 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()
@NVBCWT
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NVBCWT commented Feb 28, 2024

Yes it does on my system too. Would make changes to it , integrate speech brain probably

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