import logging import warnings import gradio as gr import pytube as pt import torch from huggingface_hub import model_info from transformers import pipeline from transformers.utils.logging import disable_progress_bar warnings.filterwarnings("ignore") disable_progress_bar() DEFAULT_MODEL_NAME = "bofenghuang/whisper-large-v2-cv11-german" # make sure no OOM MODEL_NAMES = [ "bofenghuang/whisper-medium-cv11-german", "bofenghuang/whisper-large-v2-cv11-german", ] LANG = "de" CHUNK_LENGTH_S = 30 MAX_NEW_TOKENS = 225 logging.basicConfig( format="%(asctime)s [%(levelname)s] [%(name)s] %(message)s", datefmt="%Y-%m-%dT%H:%M:%SZ", ) logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) device = 0 if torch.cuda.is_available() else "cpu" logger.info(f"Model will be loaded on device `{device}`") cached_models = {} def print_cuda_memory_info(): used_mem, tot_mem = torch.cuda.mem_get_info() logger.info(f"CUDA memory info - Free: {used_mem / 1024 ** 3:.2f} Gb, used: {(tot_mem - used_mem) / 1024 ** 3:.2f} Gb, total: {tot_mem / 1024 ** 3:.2f} Gb") def print_memory_info(): # todo if device == "cpu": pass else: print_cuda_memory_info() def maybe_load_cached_pipeline(model_name): pipe = cached_models.get(model_name) if pipe is None: # load pipeline # todo: set decoding option for pipeline pipe = pipeline( task="automatic-speech-recognition", model=model_name, chunk_length_s=CHUNK_LENGTH_S, device=device, ) # set forced_decoder_ids pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=LANG, task="transcribe") # limit genneration max length pipe.model.config.max_length = MAX_NEW_TOKENS + 1 logger.info(f"`{model_name}` pipeline has been initialized") print_memory_info() cached_models[model_name] = pipe return pipe def transcribe(microphone, file_upload, model_name): 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" file = microphone if microphone is not None else file_upload pipe = maybe_load_cached_pipeline(model_name) text = pipe(file)["text"] logger.info(f"Transcription by `{model_name}`: {text}") return warn_output + text def _return_yt_html_embed(yt_url): video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) return HTML_str def yt_transcribe(yt_url, model_name): 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") pipe = maybe_load_cached_pipeline(model_name) text = pipe("audio.mp3")["text"] logger.info(f"Transcription: {text}") return html_embed_str, text # load default model maybe_load_cached_pipeline(DEFAULT_MODEL_NAME) demo = gr.Blocks() mf_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Record"), gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Upload File"), gr.inputs.Dropdown(choices=MODEL_NAMES, default=DEFAULT_MODEL_NAME, label="Whisper Model"), ], # outputs="text", outputs=gr.outputs.Textbox(label="Transcription"), layout="horizontal", theme="huggingface", title="Whisper German Demo 🇩🇪 : Transcribe Audio", description="Transcribe long-form microphone or audio inputs with the click of a button!", allow_flagging="never", ) yt_transcribe = gr.Interface( fn=yt_transcribe, inputs=[ gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), gr.inputs.Dropdown(choices=MODEL_NAMES, default=DEFAULT_MODEL_NAME, label="Whisper Model"), ], # outputs=["html", "text"], outputs=[ gr.outputs.HTML(label="YouTube Page"), gr.outputs.Textbox(label="Transcription"), ], layout="horizontal", theme="huggingface", title="Whisper German Demo 🇩🇪 : Transcribe YouTube", description="Transcribe long-form YouTube videos with the click of a button!", allow_flagging="never", ) with demo: gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) # demo.launch(server_name="0.0.0.0", debug=True, share=True) demo.launch(enable_queue=True)