Update app.py

app.py

@@ -1,7 +1,56 @@
+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
 import numpy as np
 
+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"
+)
+
+
+def smooth_audio(audio: np.ndarray, window_size: int = 3) -> np.ndarray:
+    """
+    Simple moving average smoothing.
+    """
+    if window_size < 2:
+        return audio
+    cumsum = np.cumsum(np.insert(audio, 0, 0))
+    smoothed = (cumsum[window_size:] - cumsum[:-window_size]) / window_size
+    # pad to original length
+    pad_left = window_size // 2
+    pad_right = window_size - 1 - pad_left
+    smoothed = np.pad(smoothed, (pad_left, pad_right), mode='edge')
+    return smoothed
+
+
 @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")
 
@@ -12,17 +61,12 @@ def speak(text: str = Query(..., description="Text to convert to speech")):
     # --- Normalize ---
     peak = np.max(np.abs(audio))
     if peak > 0:
-        audio = (audio / peak) * 0.1  # match your NORMALIZATION_LEVEL
+        audio = (audio / peak) * 0.1  # Adjustable 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')
+    # --- Smooth ---
+    audio = smooth_audio(audio, window_size=3)
 
-    # Write WAV as 32-bit float
+    # --- Write WAV as 32-bit float ---
     buf = io.BytesIO()
     sf.write(buf, audio, samplerate=16000, format="WAV", subtype="FLOAT")
     buf.seek(0)