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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 transformers.utils import is_flash_attn_2_available | |
from TTS.api import TTS | |
transcriber = pipeline("automatic-speech-recognition", | |
model="openai/whisper-large-v3", | |
torch_dtype=torch.float16, | |
device="cuda:0", | |
model_kwargs={"attn_implementation": "flash_attention_2"} if is_flash_attn_2_available() else {"attn_implementation": "sdpa"}, | |
) | |
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, transcriber({"sampling_rate": sr, "raw": stream})["text"] | |
def autocomplete(text): | |
""" | |
Autocomplete the text using Gemma. | |
""" | |
if text != "": | |
response = groq_client.chat.completions.create( | |
model='gemma-7b-it', | |
messages=[{"role": "system", "content": "You are a friendly assistant named Gemma."}, | |
{"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(model_name="tts_models/fra/fairseq/vits").to("cuda") | |
api.tts_to_file(text, file_path="output.wav") | |
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() | |