app.py

import os
os.environ["TRANSFORMERS_NO_TF"] = "1"

from transformers import pipeline
import gradio as gr
from evaluate import load

# Load WER metric
wer_metric = load("wer")

# Preload multiple ASR models for comparison
models = {
    "Wav2Vec2": pipeline(
        task="automatic-speech-recognition",
        model="Devion333/wav2vec2-xls-r-300m-dv"
    ),
    "Whisper small": pipeline(
        task="automatic-speech-recognition",
        model="Devion333/whisper-small-dv-syn"
    ),
}

def transcribe(audio, chosen_models, reference):
    results = {}
    for model_name in chosen_models:
        asr_pipe = models[model_name]
        prediction = asr_pipe(audio)["text"]

        if reference.strip():
            # compute WER if reference provided
            wer = wer_metric.compute(
                predictions=[prediction.lower()],
                references=[reference.lower()]
            )
            results[model_name] = {
                "prediction": prediction,
                "WER": round(wer, 3)
            }
        else:
            results[model_name] = {
                "prediction": prediction
            }
    return results

demo = gr.Interface(
    fn=transcribe,
    inputs=[
        gr.Audio(sources=["microphone", "upload"], type="filepath", label="Upload or Record Speech"),
        gr.CheckboxGroup(choices=list(models.keys()), value=["Wav2Vec2"], label="Choose Models to Compare"),
        gr.Textbox(label="Reference Transcript (optional)")
    ],
    outputs=gr.JSON(label="Transcriptions & Statistics"),
    title="ASR Model Comparison",
    description="Upload or record audio, select ASR models, and compare their transcriptions. Optionally, provide a reference transcript to calculate WER."
)

if __name__ == "__main__":
    demo.launch()