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from flask import Flask, request, jsonify
from transformers import AutoProcessor, SeamlessM4Tv2Model
import numpy as np
import wave
import os
from huggingface_hub import InferenceClient, login
from dotenv import load_dotenv
app = Flask(__name__)
load_dotenv()
hftoken = os.getenv("HF_TOKEN")
login(token=hftoken)
processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large" )
model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
UPLOAD_FOLDER = "audio_files"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
@app.route("/", methods=["GET"])
def return_text():
return jsonify({"text": "Hello, world!"})
@app.route("/record", methods=["POST"])
def record_audio():
file = request.files['audio']
filename = os.path.join(UPLOAD_FOLDER, file.filename)
file.save(filename)
# Charger et traiter l'audio
audio_data, orig_freq = torchaudio.load(filename)
audio_inputs = processor(audios=audio_data, return_tensors="pt")
output_tokens = model.generate(**audio_inputs, tgt_lang="fra", generate_speech=False)
translated_text = processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)
return jsonify({"translated_text": translated_text})
@app.route("/text_to_speech", methods=["POST"])
def text_to_speech():
data = request.get_json()
text = data.get("text")
src_lang = data.get("src_lang")
tgt_lang = data.get("tgt_lang")
text_inputs = processor(text=text, src_lang=src_lang, return_tensors="pt")
audio_array = model.generate(**text_inputs, tgt_lang=tgt_lang)[0].cpu().numpy().squeeze()
output_filename = os.path.join(UPLOAD_FOLDER, "output.wav")
with wave.open(output_filename, "wb") as wf:
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(16000)
wf.writeframes((audio_array * 32767).astype(np.int16).tobytes())
return jsonify({"audio_url": output_filename})
if __name__ == "__main__":
app.run(debug=True)
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