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import os | |
import gradio as gr | |
import numpy as np | |
import torch | |
from groq import Groq | |
from transformers import pipeline | |
from TTS.api import TTS | |
MODEL_NAME = "openai/whisper-large-v3" | |
BATCH_SIZE = 8 | |
FILE_LIMIT_MB = 1000 | |
YT_LENGTH_LIMIT_S = 3600 # limit to 1 hour YouTube files | |
device = 0 if torch.cuda.is_available() else "cpu" | |
pipe = pipeline( | |
task="automatic-speech-recognition", | |
model=MODEL_NAME, | |
chunk_length_s=30, | |
device=device, | |
) | |
def use_pipe(inputs): | |
if inputs is None: | |
raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") | |
text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe"}, return_timestamps=True)["text"] | |
return text | |
groq_client = Groq(api_key=os.getenv('GROQ_API_KEY')) | |
def transcribe(stream, new_chunk): | |
""" | |
Transcribes using whisper | |
""" | |
sr, y = new_chunk | |
# Convert stereo to mono if necessary | |
if y.ndim == 2 and y.shape[1] == 2: | |
y = y.mean(axis=1) # Averaging both channels if stereo | |
y = y.astype(np.float32) | |
# Normalization | |
y /= np.max(np.abs(y)) | |
if stream is not None: | |
stream = np.concatenate([stream, y]) | |
else: | |
stream = y | |
return stream, use_pipe(stream) | |
def autocomplete(text): | |
""" | |
Autocomplete the text using Gemma. | |
""" | |
if text != "": | |
response = groq_client.chat.completions.create( | |
model='llama3-8b-8192', | |
messages=[{"role": "system", "content": "Tu es une assistante tres polis, tu ne repond que en francais et uniquement en utilisant le vous et jamais le tu"}, | |
{"role": "user", "content": text}] | |
) | |
return response.choices[0].message.content | |
def process_audio(input_audio, new_chunk): | |
""" | |
Process the audio input by transcribing and completing the sentences. | |
Accumulate results to return to Gradio interface. | |
""" | |
stream, transcription = transcribe(input_audio, new_chunk) | |
text = autocomplete(transcription) | |
print (transcription, text) | |
api = TTS("tts_models/multilingual/multi-dataset/xtts", gpu=True) | |
api.tts_to_file(text, file_path="output.wav", language="fr") | |
audio = "./output.wav" | |
return stream, text, audio | |
demo = gr.Interface( | |
fn = process_audio, | |
inputs = ["state", gr.Audio(sources=["microphone"], streaming=True)], | |
outputs = ["state", gr.Markdown(), gr.Audio(interactive=False, autoplay=True)], | |
title="Parlons nous ☎️", | |
description="Powered by [whisper-base-en](https://huggingface.co/openai/whisper-base.en), and [gemma-7b-it](https://huggingface.co/google/gemma-7b-it) (via [Groq](https://groq.com/))", | |
live=True, | |
allow_flagging="never" | |
) | |
demo.launch() | |