app.py

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()