run_demo_hf_multiple_models.py

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-french"
# make sure no OOM
MODEL_NAMES = [
    "bofenghuang/whisper-medium-cv11-french",
    "bofenghuang/whisper-large-v2-cv11-french",
]
CHUNK_LENGTH_S = 30
# STRIDE_LENGTH_S = 0
# 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,
            # stride_length_s=STRIDE_LENGTH_S,
            device=device,
        )
        # set forced_decoder_ids
        pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="fr", 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'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
        " </center>"
    )
    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 by `{model_name}` of "{yt_url}": {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 French 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 French 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)