Create otherApp.py

otherApp.py

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+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"
+)
+
+
+@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
+    buf = io.BytesIO()
+    sf.write(buf, speech.numpy(), samplerate=16000, format="WAV")
+    buf.seek(0)
+
+    return StreamingResponse(buf, media_type="audio/wav")