Create app.py

app.py

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+from fastapi import FastAPI
+from fastapi.responses import StreamingResponse
+from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech
+import torch
+import soundfile as sf
+import io
+
+app = FastAPI()
+
+# Load processor & model
+processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
+model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
+
+# Dummy speaker embedding (flat voice). You can later replace with real embeddings.
+speaker_embeddings = torch.zeros((1, 512))
+
+@app.get("/speak")
+def speak(text: str):
+    inputs = processor(text=text, return_tensors="pt")
+
+    # Generate speech
+    speech = model.generate_speech(inputs["input_ids"], speaker_embeddings)
+
+    # Save into memory buffer
+    buf = io.BytesIO()
+    sf.write(buf, speech.numpy(), 16000, format="WAV")
+    buf.seek(0)
+
+    return StreamingResponse(buf, media_type="audio/wav")