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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_v2", gpu=True)
api.tts_to_file(text, file_path="output.wav", speaker="Ana Florence",language="fr", split_sentences=True)
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()
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