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| from nemo.collections.asr.models import ASRModel | |
| 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-tdt-1.1b" | |
| YT_LENGTH_LIMIT_S=3600 | |
| model = ASRModel.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 = ( | |
| "<p style='text-align: center'>" | |
| "<a href='https://huggingface.co/nvidia/parakeet-tdt-1.1b' target='_blank'>🎙️ Learn more about Parakeet TDT model</a> | " | |
| "<a href='https://arxiv.org/abs/2304.06795' target='_blank'>📚 TDT ICML paper</a> | " | |
| "<a href='https://github.com/NVIDIA/NeMo' target='_blank'>🧑💻 Repository</a>" | |
| "</p>" | |
| ) | |
| 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'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' | |
| " </center>" | |
| ) | |
| 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 TDT 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. TDT models are 75% more efficient than similar size RNNT model" | |
| ), | |
| 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 TDT 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. TDT models are 75% more efficient than similar size RNNT model" | |
| ), | |
| 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 TDT 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. TDT models are 75% more efficient than similar size RNNT model" | |
| ), | |
| allow_flagging="never", | |
| ) | |
| with demo: | |
| gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"]) | |
| demo.launch() | |