Update app.py

app.py

@@ -1,49 +1,30 @@
-from fastapi import FastAPI, Query
-from fastapi.responses import StreamingResponse
-from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
-import torch
-import io
-import soundfile as sf
-import requests
-
-app = FastAPI(title="SpeechT5 TTS API")
-
-# Load models once at startup
-processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
-model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
-vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
-
-
-# Function to load a speaker embedding from a URL
-def load_speaker_embedding(url: str) -> torch.Tensor:
-    response = requests.get(url)
-    response.raise_for_status()
-    # Load the .bin file as a float32 tensor
-    embedding = torch.frombuffer(response.content, dtype=torch.float32)
-    return embedding.unsqueeze(0)  # Add batch dimension
-
-
-# Example: load US female 1
-speaker_embeddings = load_speaker_embedding(
-    "https://huggingface.co/datasets/Xenova/cmu-arctic-xvectors-extracted/resolve/main/cmu_us_slt_arctic-wav-arctic_a0001.bin"
-)
-
+import numpy as np
 
 @app.get("/speak")
 def speak(text: str = Query(..., description="Text to convert to speech")):
-    """
-    Convert text to speech using SpeechT5 + HiFi-GAN.
-    Returns a WAV audio stream.
-    """
     # Prepare input
     inputs = processor(text=text, return_tensors="pt")
 
     # Generate speech
     speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
-
-    # Convert to bytes buffer
+    audio = speech.numpy().astype(np.float32)
+
+    # --- Normalize ---
+    peak = np.max(np.abs(audio))
+    if peak > 0:
+        audio = (audio / peak) * 0.1  # match your NORMALIZATION_LEVEL
+
+    # --- Smooth (moving average) ---
+    window_size = 3  # like SMOOTHING_WINDOW
+    if window_size > 1:
+        cumsum = np.cumsum(np.insert(audio, 0, 0)) 
+        audio = (cumsum[window_size:] - cumsum[:-window_size]) / window_size
+        # pad to original length
+        audio = np.pad(audio, (window_size//2, window_size-1-window_size//2), mode='edge')
+
+    # Write WAV as 32-bit float
     buf = io.BytesIO()
-    sf.write(buf, speech.numpy(), samplerate=16000, format="WAV")
+    sf.write(buf, audio, samplerate=16000, format="WAV", subtype="FLOAT")
     buf.seek(0)
 
     return StreamingResponse(buf, media_type="audio/wav")