test

Dockerfile

@@ -13,4 +13,4 @@ COPY --chown=user ./requirements.txt requirements.txt
 RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 COPY --chown=user . /app
-CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+CMD ["python", "app.py"]

app.py

@@ -1,52 +1,55 @@
-from fastapi import FastAPI, File, UploadFile, HTTPException
+from flask import Flask, request, jsonify
 from transformers import AutoProcessor, SeamlessM4Tv2Model
 import numpy as np
 import wave
 import os
-from starlette.responses import JSONResponse
+from huggingface_hub import InferenceClient, login
+app = Flask(__name__)
 
-app = FastAPI()
+ 
 
-processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
-model = SeamlessM4Tv2Model.from_pretrained("facebook/wav2vec2-base-960h")
+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.get("/")
+@app.route("/", methods=["GET"])
 def return_text():
-    return {"text": "Hello, world!"}
+    return jsonify({"text": "Hello, world!"})
 
-@app.post("/record")
-async def record_audio(audio: UploadFile = File(...)):
-    filename = os.path.join(UPLOAD_FOLDER, audio.filename)
-    with open(filename, "wb") as buffer:
-        buffer.write(await audio.read())
+@app.route("/record", methods=["POST"])
+def record_audio():
+    file = request.files['audio']
+    filename = os.path.join(UPLOAD_FOLDER, file.filename)
+    file.save(filename)
     
-    try:
-        # 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)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Erreur de transcription: {str(e)}")
+    # 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 JSONResponse(content={"translated_text": translated_text})
+    return jsonify({"translated_text": translated_text})
 
-@app.post("/text_to_speech")
-async def text_to_speech(text: str, src_lang: str, tgt_lang: str):
-    try:
-        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()
+@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")
     
-        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())
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Erreur de synthèse vocale: {str(e)}")
+    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()
     
-    return JSONResponse(content={"audio_url": output_filename})
+    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)