add parler

app.py

@@ -1,6 +1,12 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
 from typing import List
+import torch
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer
+import soundfile as sf
+import io
+from starlette.responses import StreamingResponse
 
 # Initialize the FastAPI app
 app = FastAPI()
@@ -11,24 +17,48 @@ class Item(BaseModel):
     name: str
     section: str
 
+# Initialize ParlerTTS
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1").to(device)
+tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler-tts-mini-v1")
+
 # A simple GET endpoint
 @app.get("/")
 def greet_json():
     return {"Hello": "World!"}
 
+# Function to generate audio from text using ParlerTTS
+def generate_audio(text, description="Neutral voice"):
+    input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
+    prompt_input_ids = tokenizer(text, return_tensors="pt").input_ids.to(device)
+    generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+    audio_arr = generation.cpu().numpy().squeeze()
+    return audio_arr, model.config.sampling_rate
+
 # A POST endpoint to receive and parse an array of JSON objects
 @app.post("/")
-def create_items(items: List[Item]):
-    # Process each item in the list
+async def create_items(items: List[Item]):
     processed_items = []
     for item in items:
-        # Here you could perform any processing you need on each item
+        # Generate audio
+        audio_arr, sample_rate = generate_audio(item.text)
+
+        # Create in-memory bytes buffer for audio
+        audio_bytes = io.BytesIO()
+        sf.write(audio_bytes, audio_arr, sample_rate, format="WAV")
+        audio_bytes.seek(0)  # Reset buffer position
+
         processed_item = {
             "text": item.text,
             "name": item.name,
             "section": item.section,
-            "processed": True  # Example of adding a field to indicate processing
+            "processed": True,
+            "audio": StreamingResponse(audio_bytes, media_type="audio/wav")
         }
         processed_items.append(processed_item)
-    
+
     return {"processed_items": processed_items}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8000)

requirements.txt

@@ -1,2 +1,6 @@
 fastapi
 uvicorn[standard]
+torch
+transformers
+parler-tts
+soundfile