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# https://github.com/openai/whisper/discussions/categories/show-and-tell
import wavio as wv
import datetime
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
import torch
from dotenv import load_dotenv
import os
import whisper
import ffmpeg
import gradio as gr
from transformers import pipeline
p = pipeline("automatic-speech-recognition")
basedir = os.path.abspath(os.path.dirname(__file__))
load_dotenv(os.path.join(basedir, '.env'))
OPENAI_API_KEY=os.getenv("OPENAI_API_KEY")
whisper_model = whisper.load_model("base")
# this model was loaded from https://hf.co/models
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
device = 0 if torch.cuda.is_available() else -1
LANGS = ["ace_Arab", "eng_Latn", "fra_Latn", "spa_Latn", "yue_Hant","zho_Hans","zho_Hant"]
LANGS_source = ["eng_Latn"]
# Yue Chinese - yue_Hant, Chinese (Simplified)-Zho_Hans, Chinese(Traditional)-zho_Hant
# https://github.com/facebookresearch/flores/tree/main/flores200#languages-in-flores-200
def translate(text, src_lang, tgt_lang):
"""
Translate the text from source lang to target lang
"""
translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer, src_lang=src_lang, tgt_lang=tgt_lang, max_length=400, device=device)
result = translation_pipeline(text)
return result[0]['translation_text']
def transcribe(audio):
# text_audio = p(audio)["text"]
# text_for_audio = whisper_model.transcribe(audio)
# text_from_whisper = text_for_audio["text"]
# text=translate(text_from_whisper,"eng_Latn","zho_Hans")
text="abc"
return text
gr.Interface(
fn=transcribe,
inputs=gr.Audio(source="microphone", type="filepath"),
outputs="text").launch()
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