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| import torch | |
| import gradio as gr | |
| import pytube as pt | |
| from transformers import pipeline | |
| from huggingface_hub import model_info | |
| #from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
| # MODEL_NAME = "ihanif/pashto-asr-v3" | |
| MODEL_NAME = "ihanif/whisper-small-tunning-v2" #"ihanif/pashto-asr-v5" | |
| lang = "ps" | |
| #load pre-trained model and tokenizer | |
| #processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME) | |
| #model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME) | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=MODEL_NAME, | |
| #chunk_length_s=30, | |
| device=device, | |
| ) | |
| #pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe") | |
| def transcribe(microphone, file_upload): | |
| warn_output = "" | |
| # if (microphone is not None) and (file_upload is not None): | |
| # warn_output = ( | |
| # "WARNING: You've uploaded an audio file and used the microphone. " | |
| # "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" | |
| # ) | |
| # elif (microphone is None) and (file_upload is None): | |
| # return "ERROR: You have to either use the microphone or upload an audio file" | |
| if (microphone is None) and (file_upload is None): | |
| return "ERROR: You have to either use the microphone or upload an audio file" | |
| file = microphone if microphone is not None else file_upload | |
| text = pipe(file)["text"] | |
| #transcription = wav2vec_model(audio)["text"] | |
| return warn_output + text | |
| def _return_yt_html_embed(yt_url): | |
| video_id = yt_url.split("?v=")[-1] | |
| HTML_str = ( | |
| f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' | |
| " </center>" | |
| ) | |
| return HTML_str | |
| def yt_transcribe(yt_url): | |
| yt = pt.YouTube(yt_url) | |
| html_embed_str = _return_yt_html_embed(yt_url) | |
| stream = yt.streams.filter(only_audio=True)[0] | |
| stream.download(filename="audio.mp3") | |
| text = pipe("audio.mp3")["text"] | |
| return html_embed_str, text | |
| demo = gr.Blocks() | |
| examples=[["example-1.wav", "example-1.wav"],["example-2.wav", "example-2.wav"]] | |
| # examples=["example-1.wav"] | |
| mf_transcribe = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.inputs.Audio(source="microphone", type="filepath", optional=True), | |
| gr.inputs.Audio(source="upload", type="filepath", optional=True), | |
| ], | |
| outputs="text", | |
| layout="horizontal", | |
| theme="huggingface", | |
| title="(Pashto ASR) د پښتو اتوماتیک وینا پیژندنه", | |
| description=( | |
| "</p> تاسو کولی شئ یو آډیو فایل اپلوډ کړئ یا په خپل وسیله مایکروفون وکاروئ. مهرباني وکړئ ډاډ ترلاسه کړئ چې تاسو اجازه ورکړې ده<p>" | |
| ), | |
| #allow_flagging="never", | |
| flagging_options=["Transcription is not in Pashto", "Transcription is wrong"], | |
| examples=examples, | |
| ) | |
| mf_transcribe.launch() | |
| #with demo: | |
| # gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) | |
| #demo.launch(enable_queue=False) | |