app.py

import gradio as gr
import numpy as np
import torch
import transformers
from pathlib import Path
from transformers import pipeline
from transformers.utils import logging

# Log

#logging.set_verbosity_debug()
logger = logging.get_logger("transformers")

# Pipelines

device = 0 if torch.cuda.is_available() else "cpu"

## Automatic Speech Recognition
## https://huggingface.co/docs/transformers/task_summary#automatic-speech-recognition
## Require ffmpeg to be installed

asr_model = "openai/whisper-tiny"
asr = pipeline(
    "automatic-speech-recognition",
    model=asr_model,
#    torch_dtype=torch.float16,
    device=device
)

## Token Classification / Name Entity Recognition
## https://huggingface.co/docs/transformers/task_summary#token-classification
tc_model = "dslim/distilbert-NER"
tc = pipeline(
    "token-classification", # ner
    model=tc_model,
    device=device
)

# ---

# Transformers

# https://www.gradio.app/main/docs/gradio/audio#behavior
# As output component: expects audio data in any of these formats:
# - a str or pathlib.Path filepath
# - or URL to an audio file,
# - or a bytes object (recommended for streaming),
# - or a tuple of (sample rate in Hz, audio data as numpy array)
def transcribe(audio: str | Path | bytes | tuple[int, np.ndarray] | None):
    if audio is None:
        return "..."
    # TODO Manage str/Path

    logger.debug("Transcribe")

    text = ""
    # https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline.__call__
    # Whisper input format for tuple differ from output provided by gradio audio component
    if asr_model.startswith("openai/whisper"):
        inputs = {"sampling_rate": audio[0], "raw": audio[1]} if type(audio) is tuple else audio
        transcript = asr(inputs)
        text = transcript['text']

    logger.debug("Tokenize:[" + text + "]")

    entities = tc(text)

    #logger.debug("Classify:[" + entities + "]")

    # TODO Add Text Classification for sentiment analysis
    return {"text": text, "entities": entities}

# ---

# Gradio

## Interfaces

# https://www.gradio.app/main/docs/gradio/audio
input_audio = gr.Audio(
    sources=["upload", "microphone"],
    show_share_button=False
)

## App

gradio_app = gr.Interface(
    transcribe,
    inputs=[
        input_audio
    ],
    outputs=[
        gr.HighlightedText()
    ],
    title="ASRNERSBX",
    description=(
        "Transcribe, Tokenize, Classify"
    ),
    flagging_mode="never"
)

## Start!
gradio_app.launch()