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import torch
import torchaudio
import gradio as gr
import soundfile as sf
import wave
import numpy as np
from transformers import WhisperForCTC, WhisperProcessor, AutoModelForSeq2SeqLM, AutoTokenizer
from transformers import OpenVoiceV2Processor, OpenVoiceV2
# Load ASR model and processor
processor_asr = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
model_asr = WhisperForCTC.from_pretrained("openai/whisper-large-v3")
# Load text-to-text model and tokenizer
text_model = AutoModelForSeq2SeqLM.from_pretrained("meta-llama/Meta-Llama-3-8B")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B")
# Load TTS model
tts_processor = OpenVoiceV2Processor.from_pretrained("myshell-ai/OpenVoiceV2")
tts_model = OpenVoiceV2.from_pretrained("myshell-ai/OpenVoiceV2")
@spaces.GPU()
# ASR function
def transcribe(audio):
waveform, sample_rate = torchaudio.load(audio)
inputs = processor_asr(waveform, sampling_rate=sample_rate, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model_asr(inputs.input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor_asr.batch_decode(predicted_ids)
return transcription[0]
@spaces.GPU()
# Text-to-text function
def generate_response(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
outputs = text_model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
@spaces.GPU()
# TTS function
def synthesize_speech(text):
inputs = tts_processor(text, return_tensors="pt")
with torch.no_grad():
mel_outputs, mel_outputs_postnet, _, alignments = tts_model.inference(inputs.input_ids)
audio = tts_model.infer(mel_outputs_postnet)
return audio
@spaces.GPU()
# Real-time processing function
def real_time_pipeline():
# Adjust this part to handle live recording using soundfile and play back using simpleaudio
import simpleaudio as sa
import tempfile
import time
wake_word = "hello mate"
wake_word_detected = False
print("Listening for wake word...")
with tempfile.NamedTemporaryFile(delete=False) as tmp_wav_file:
tmp_wav_path = tmp_wav_file.name
try:
while True:
# Capture audio here (this is a simplified example, you need actual audio capture logic)
time.sleep(2) # Simulate 2 seconds of audio capture
# Save the captured audio to the temp file for ASR
data, sample_rate = sf.read(tmp_wav_path)
sf.write(tmp_wav_path, data, sample_rate)
# Step 1: Transcribe audio to text
transcription = transcribe(tmp_wav_path).lower()
if wake_word in transcription:
wake_word_detected = True
print("Wake word detected. Processing audio...")
while wake_word_detected:
# Capture audio here (this is a simplified example, you need actual audio capture logic)
time.sleep(2) # Simulate 2 seconds of audio capture
# Save the captured audio to the temp file for ASR
data, sample_rate = sf.read(tmp_wav_path)
sf.write(tmp_wav_path, data, sample_rate)
# Step 1: Transcribe audio to text
transcription = transcribe(tmp_wav_path)
# Step 2: Generate response using text-to-text model
response = generate_response(transcription)
# Step 3: Synthesize speech from text
synthesized_audio = synthesize_speech(response)
# Save the synthesized audio to a temporary file
output_path = "output.wav"
torchaudio.save(output_path, synthesized_audio.squeeze(1), 22050)
# Play the synthesized audio using simpleaudio
wave_obj = sa.WaveObject.from_wave_file(output_path)
play_obj = wave_obj.play()
play_obj.wait_done()
except KeyboardInterrupt:
print("Stopping...")
# Gradio interface
gr_interface = gr.Interface(
fn=real_time_pipeline,
inputs=None,
outputs=None,
live=True,
title="Real-Time Audio-to-Audio Model",
description="ASR + Text-to-Text Model + TTS with Human-like Voice and Emotions"
)
iface.launch(inline=False) |