Add application file

Dockerfile

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+FROM python:3.9-slim-buster
+
+# Cài đặt thư viện hệ thống cần thiết cho soundfile
+RUN apt-get update && apt-get install -y libsndfile1 && rm -rf /var/lib/apt/lists/*
+
+# Tạo user mới để phù hợp với chính sách bảo mật của Hugging Face
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:${PATH}"
+
+WORKDIR /app
+
+# Copy và cài đặt thư viện
+COPY --chown=user requirements.txt .
+RUN pip install --no-cache-dir --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Copy toàn bộ code
+COPY --chown=user . .
+
+# Hugging Face Spaces mặc định chạy trên port 7860
+EXPOSE 7860
+
+# Chạy ứng dụng trên port 7860
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import VitsModel, AutoTokenizer
+import torch
+import soundfile as sf
+import io
+import base64
+import os
+
+app = FastAPI(
+    title="MMS-TTS Vietnamese API",
+    description="A simple API for Vietnamese Text-to-Speech using facebook/mms-tts-vie."
+)
+
+# Define the request body model
+class TTSRequest(BaseModel):
+    text: str
+    speaker_id: int = 0 # MMS-TTS models are single-speaker by default, but keeping this for potential future multi-speaker models
+    speed_factor: float = 1.0 # Optional: Adjust speech speed
+
+# Global variables to hold the loaded model and tokenizer
+# Avoid reloading them for every request, improving performance.
+model = None
+tokenizer = None
+
+@app.on_event("startup")
+async def startup_event():
+    """
+    Load the TTS model and tokenizer when the FastAPI application starts up.
+    This ensures they are ready for immediate use and not reloaded per request.
+    """
+    global model, tokenizer
+    try:
+        print("Loading MMS-TTS model 'facebook/mms-tts-vie'...")
+        model = VitsModel.from_pretrained("facebook/mms-tts-vie")
+        tokenizer = AutoTokenizer.from_pretrained("facebook/mms-tts-vie")
+        print("MMS-TTS model and tokenizer loaded successfully.")
+    except Exception as e:
+        # Log the full exception for debugging
+        import traceback
+        traceback.print_exc()
+        print(f"ERROR: Failed to load MMS-TTS model or tokenizer on startup: {e}")
+        model = None # Ensure they are clearly unset if loading fails
+        tokenizer = None
+
+@app.get("/")
+async def read_root():
+    """Basic health check endpoint."""
+    return {"message": "MMS-TTS Vietnamese API is running!", "model_loaded": model is not None}
+
+
+@app.post("/synthesize_speech")
+async def synthesize_speech(request: TTSRequest):
+    """
+    Synthesizes speech from the given text and returns it as a Base64 encoded WAV byte array.
+    """
+    if model is None or tokenizer is None:
+        raise HTTPException(
+            status_code=503,
+            detail="TTS model is not loaded. Please try again later or check server logs."
+        )
+
+    try:
+        # Tokenize the input text
+        # Ensure the text is properly handled by the tokenizer for Vietnamese
+        inputs = tokenizer(request.text, return_tensors="pt")
+
+        # Generate speech
+        with torch.no_grad():
+            # speaker_id is usually not needed for single-speaker models like mms-tts-vie
+            # if model supports it, you might pass speaker_id=request.speaker_id
+            audio_values = model(**inputs).waveform
+
+        # Convert to WAV bytes in memory
+        # The sampling rate is critical and should match the model's config
+        samplerate = model.config.sampling_rate
+        output_buffer = io.BytesIO()
+        sf.write(output_buffer, audio_values.numpy().squeeze(), samplerate, format='WAV')
+        output_buffer.seek(0) # Rewind the buffer to the beginning
+
+        # Encode the WAV bytes to Base64 string
+        audio_base64 = base64.b64encode(output_buffer.read()).decode('utf-8')
+
+        return {"audio_base64": audio_base64}
+
+    except Exception as e:
+        import traceback
+        traceback.print_exc() # Print full traceback to console for debugging
+        raise HTTPException(
+            status_code=500,
+            detail=f"An error occurred during speech synthesis: {e}"
+        )

requirements.txt

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+--extra-index-url https://download.pytorch.org/whl/cpu
+torch
+transformers
+fastapi
+uvicorn
+pydantic
+soundfile