Spaces:
Sleeping
Sleeping
| #This code for CPU | |
| #import torch | |
| #from transformers import AutoTokenizer, TextStreamer, pipeline | |
| import whisper | |
| #transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en") | |
| model = whisper.load_model("small") | |
| import gradio as gr | |
| import time | |
| import googletrans | |
| from googletrans import Translator | |
| translator = Translator() | |
| lan = googletrans.LANGUAGES | |
| #print(lan) | |
| keys = list(lan.keys()) | |
| vals = list(lan.values()) | |
| def transcribe(lang,audio): | |
| #time.sleep(3) | |
| print(lang) | |
| # load audio and pad/trim it to fit 30 seconds | |
| audio = whisper.load_audio(audio) | |
| audio = whisper.pad_or_trim(audio) | |
| # make log_Mel spectrogram and move to the same device as the model | |
| mel = whisper.log_mel_spectrogram(audio).to(model.device) | |
| # detect the spoken language | |
| _, probs = model.detect_language(mel) | |
| #print(f"Detected language: {max(probs, key=probs.get)}") | |
| # decode the audio | |
| options = whisper.DecodingOptions() | |
| result = whisper.decode(model, mel, options, fp16=False) | |
| lang = lang.lower() | |
| #state += translator.translate(result.text,dest=keys[vals.index(lang)]).text + " " | |
| return translator.translate(result.text,dest=keys[vals.index(lang)]).text | |
| def clear(msg): | |
| return "" | |
| with gr.Blocks() as demo: | |
| state = gr.State(value="") | |
| audio = gr.Audio(label="press start record to speek",source="microphone", type="filepath") | |
| dropdown = gr.Dropdown(label="first select the destination language",choices=vals) | |
| msg = gr.Textbox() | |
| clearBTN = gr.Button("Clear") | |
| dropdown.select(transcribe, [dropdown,audio], outputs=[msg]) | |
| #audio.stream(transcribe, [dropdown,audio,state], outputs=[msg,state]) | |
| #audio.stop_recording(clear, [state], outputs=[msg,state]) | |
| clearBTN.click(clear, [msg], outputs=[msg]) | |
| demo.launch(share=True) |