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| import torch | |
| import spaces | |
| import gradio as gr | |
| import soundfile as sf | |
| import numpy as np | |
| import pytube as pt | |
| import librosa | |
| from transformers import AutoProcessor, Wav2Vec2BertForCTC | |
| MODEL_NAME = "mikr/w2v-bert-2.0-czech-colab-cv16" | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| processor = AutoProcessor.from_pretrained(MODEL_NAME) | |
| model = Wav2Vec2BertForCTC.from_pretrained(MODEL_NAME).to(device) | |
| def text_from_audio(audio_path): | |
| a, s = librosa.load(audio_path, sr=16_000) | |
| input_values = processor(a, sampling_rate=s, return_tensors="pt").input_features | |
| with torch.no_grad(): | |
| logits = model(input_values.to(device)).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| # transcribe speech | |
| transcription = processor.batch_decode(predicted_ids) | |
| text = transcription[0] | |
| return text | |
| 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" | |
| audio_path = microphone if microphone is not None else file_upload | |
| text = text_from_audio(audio_path) | |
| 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 = text_from_audio("audio.mp3") | |
| return html_embed_str, text | |
| demo = gr.Blocks() | |
| mf_transcribe = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.Audio(sources="microphone", type="filepath"), | |
| gr.Audio(sources="upload", type="filepath"), | |
| ], | |
| outputs="text", | |
| title="W2V Bert 2.0 Demo: Transcribe Czech Audio", | |
| description=( | |
| "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the fine-tuned" | |
| f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) " | |
| "and 🤗 Transformers to transcribe audio files of arbitrary length." | |
| ), | |
| allow_flagging="never", | |
| ) | |
| yt_transcribe = gr.Interface( | |
| fn=yt_transcribe, | |
| inputs=[gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")], | |
| outputs=["html", "text"], | |
| title="W2V Bert 2.0 Demo: Transcribe Czech YouTube Video", | |
| description=( | |
| "Transcribe long-form YouTube videos with the click of a button! Demo uses the the fine-tuned checkpoint:" | |
| f" [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files of" | |
| " arbitrary length." | |
| ), | |
| allow_flagging="never", | |
| ) | |
| with demo: | |
| gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) | |
| demo.launch(server_name="0.0.0.0") | |