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README.md

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+# ASR + LLM + TTS REST API
+
+This is a simple RESTful API built with FastAPI.
+
+## Endpoints
+
+- `/asr/`: Upload a `.wav` file and get the transcription.
+- `/llm/`: Send a prompt and get generated text.
+- `/tts/`: Send text and get back audio path (WAV file).
+
+## Run
+
+```bash
+pip install -r requirements.txt
+python app.py
+```

app.py

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+from fastapi import FastAPI, UploadFile, File
+from pydantic import BaseModel
+import torch
+import torchaudio
+from transformers import pipeline, AutoProcessor, AutoModelForSpeechSeq2Seq, AutoTokenizer, AutoModelForSeq2SeqLM
+from TTS.api import TTS
+import uvicorn
+import tempfile
+
+app = FastAPI()
+
+# Load ASR (Whisper small)
+asr_processor = AutoProcessor.from_pretrained("openai/whisper-small")
+asr_model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small").to("cpu")
+
+# Load LLM (Flan-T5 small)
+llm_tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
+llm_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small").to("cpu")
+
+# Load TTS (Facebook MMS)
+tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts")  # lightweight multilingual
+
+class LLMInput(BaseModel):
+    prompt: str
+
+@app.post("/asr/")
+async def transcribe(file: UploadFile = File(...)):
+    with tempfile.NamedTemporaryFile(delete=False) as tmp:
+        tmp.write(await file.read())
+        waveform, rate = torchaudio.load(tmp.name)
+        if rate != 16000:
+            resampler = torchaudio.transforms.Resample(orig_freq=rate, new_freq=16000)
+            waveform = resampler(waveform)
+        inputs = asr_processor(waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt")
+        with torch.no_grad():
+            predicted_ids = asr_model.generate(inputs["input_features"])
+        transcription = asr_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+    return {"transcription": transcription}
+
+@app.post("/llm/")
+async def generate(input: LLMInput):
+    input_ids = llm_tokenizer.encode(input.prompt, return_tensors="pt")
+    output_ids = llm_model.generate(input_ids, max_length=100)
+    response = llm_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return {"response": response}
+
+@app.post("/tts/")
+async def synthesize(input: LLMInput):
+    path = tts.tts_to_file(text=input.prompt, file_path="output.wav")
+    return {"audio_path": path}
+
+if __name__ == "__main__":
+    uvicorn.run("app:app", host="0.0.0.0", port=7860)

requirements.txt

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+fastapi
+uvicorn
+transformers
+torchaudio
+TTS
+torch

utils.py

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+# Utility functions can go here