main.py

import time
from scipy.io.wavfile import write, read
import numpy as np


# from typing import Union
# from pydantic import BaseModel

from fastapi import FastAPI, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import FileResponse

# from fastapi.staticfiles import StaticFiles
# from fastapi.responses import FileResponse

import torch
# from transformers import pipeline

from transformers import SeamlessM4Tv2Model
from transformers import AutoProcessor

model_name = "facebook/seamless-m4t-v2-large"
# model_name = "facebook/hf-seamless-m4t-medium"

processor = AutoProcessor.from_pretrained(model_name)
model = SeamlessM4Tv2Model.from_pretrained(model_name)


device = "cuda:0" if torch.cuda.is_available() else "cpu"
# torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

model.to(device)

app = FastAPI(docs_url="/api/docs")

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
    allow_credentials=True,
)

BATCH_SIZE = 8


@app.get("/device")
def getDevice():
    start_time = time.time()
    print("Time took to process the request and return response is {} sec".format(
        time.time() - start_time))
    return device


@app.get("/translate")
def transcribe(inputs, src_lang="eng", tgt_lang="por"):
    start_time = time.time()

    if inputs is None:
        raise "No audio file submitted! Please upload or record an audio file before submitting your request."

    text_inputs = processor(text=inputs,
                            src_lang=src_lang, return_tensors="pt").to(device)

    output_tokens = model.generate(
        **text_inputs, tgt_lang=tgt_lang, generate_speech=False)

    translated_text_from_text = processor.decode(
        output_tokens[0].tolist()[0], skip_special_tokens=True)

    print("Time took to process the request and return response is {} sec".format(
        time.time() - start_time))
    return translated_text_from_text


@app.get("/audio")
async def audio(inputs, src_lang="eng", tgt_lang="por", speaker_id=5):
    start_time = time.time()

    if inputs is None:
        raise "No audio file submitted! Please upload or record an audio file before submitting your request."

    text_inputs = processor(text=inputs,
                            src_lang=src_lang, return_tensors="pt").to(device)

    audio_array_from_text = model.generate(
        **text_inputs, tgt_lang=tgt_lang, speaker_id=int(speaker_id))[0].cpu().numpy().squeeze()

    write(f"/tmp/output{start_time}.wav", model.config.sampling_rate,
          audio_array_from_text)

    print("Time took to process the request and return response is {} sec".format(
        time.time() - start_time))

    return FileResponse(f"/tmp/output{start_time}.wav", media_type="audio/mpeg")


@app.post("/transcribe-audio")
async def transcribe_audio(soundFile: UploadFile, tgt_lang='eng'):
    start_time = time.time()

    with open(f"/tmp/{soundFile.filename}", "wb") as buffer:
        buffer.write(soundFile.file.read())

    sample_rate, audio_data = read(f"/tmp/{soundFile.filename}")

    audio_inputs = processor(
        audios=audio_data, return_tensors="pt").to(device)

    audio_array_from_audio = model.generate(
        **audio_inputs, tgt_lang=tgt_lang)[0].cpu().numpy().squeeze()

    write(f"/tmp/output{start_time}.wav", model.config.sampling_rate,
          audio_array_from_audio)

    print("Time took to process the request and return response is {} sec".format(
        time.time() - start_time))

    return FileResponse(f"/tmp/output{start_time}.wav", media_type="audio/wav")