from nemo.collections.asr.models import EncDecRNNTBPEModel import yt_dlp as youtube_dl import os import tempfile import torch import gradio as gr from pydub import AudioSegment device = "cuda" if torch.cuda.is_available() else "cpu" MODEL_NAME="nvidia/parakeet-rnnt-1.1b" YT_LENGTH_LIMIT_S=3600 model = EncDecRNNTBPEModel.from_pretrained(model_name=MODEL_NAME).to(device) model.eval() def get_transcripts(audio_path): text = model.transcribe([audio_path])[0][0] return text article = ( "

" "🎙️ Learn more about Parakeet model | " "📚 FastConformer paper | " "🧑‍💻 Repository" "

" ) examples = [ ["data/conversation.wav"], ["data/id10270_5r0dWxy17C8-00001.wav"], ] def _return_yt_html_embed(yt_url): video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) return HTML_str def download_yt_audio(yt_url, filename): info_loader = youtube_dl.YoutubeDL() try: info = info_loader.extract_info(yt_url, download=False) except youtube_dl.utils.DownloadError as err: raise gr.Error(str(err)) file_length = info["duration_string"] file_h_m_s = file_length.split(":") file_h_m_s = [int(sub_length) for sub_length in file_h_m_s] if len(file_h_m_s) == 1: file_h_m_s.insert(0, 0) if len(file_h_m_s) == 2: file_h_m_s.insert(0, 0) file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2] if file_length_s > YT_LENGTH_LIMIT_S: yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S)) file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s)) raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.") ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"} with youtube_dl.YoutubeDL(ydl_opts) as ydl: try: ydl.download([yt_url]) except youtube_dl.utils.ExtractorError as err: raise gr.Error(str(err)) def yt_transcribe(yt_url, max_filesize=75.0): html_embed_str = _return_yt_html_embed(yt_url) with tempfile.TemporaryDirectory() as tmpdirname: filepath = os.path.join(tmpdirname, "video.mp4") download_yt_audio(yt_url, filepath) audio = AudioSegment.from_file(filepath) wav_filepath = os.path.join(tmpdirname, "audio.wav") audio.export(wav_filepath, format="wav") text = get_transcripts(wav_filepath) return html_embed_str, text demo = gr.Blocks() mf_transcribe = gr.Interface( fn=get_transcripts, inputs=[ gr.Audio(sources="microphone", type="filepath") ], outputs="text", theme="huggingface", title="Parakeet RNNT 1.1B: Transcribe Audio", description=( "Transcribe microphone or audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and NVIDIA NeMo to transcribe audio files" " of arbitrary length." ), allow_flagging="never", ) file_transcribe = gr.Interface( fn=get_transcripts, inputs=[ gr.Audio(sources="upload", type="filepath", label="Audio file"), ], outputs="text", theme="huggingface", title="Parakeet RNNT 1.1B: Transcribe Audio", description=( "Transcribe microphone or audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and NVIDIA NeMo(https://github.com/NVIDIA/NeMo) to transcribe audio files" " of arbitrary length." ), allow_flagging="never", ) youtube_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"], theme="huggingface", title="Parakeet RNNT 1.1B: Transcribe Audio", description=( "Transcribe microphone or audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and NVIDIA NeMo to transcribe audio files" " of arbitrary length." ), allow_flagging="never", ) with demo: gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"]) demo.launch()