app.py

import spaces
import jiwer
import numpy as np
import re
import gradio as gr

def split_into_sentences(text):
    """
    Simple sentence tokenizer using regular expressions.
    Splits text into sentences based on punctuation.
    """
    sentences = re.split(r'(?<=[.!?])\s*', text)
    sentences = [s.strip() for s in sentences if s.strip()]
    return sentences

@spaces.GPU()
def calculate_wer(reference, hypothesis):
    """
    Calculate the Word Error Rate (WER) using jiwer.
    """
    wer = jiwer.wer(reference, hypothesis)
    return wer

@spaces.GPU()
def calculate_cer(reference, hypothesis):
    """
    Calculate the Character Error Rate (CER) using jiwer.
    """
    cer = jiwer.cer(reference, hypothesis)
    return cer

@spaces.GPU()
def calculate_sentence_metrics(reference, hypothesis):
    """
    Calculate WER and CER for each sentence and overall statistics.
    """
    try:
        reference_sentences = split_into_sentences(reference)
        hypothesis_sentences = split_into_sentences(hypothesis)

        if len(reference_sentences) != len(hypothesis_sentences):
            raise ValueError("Reference and hypothesis must contain the same number of sentences")

        sentence_wers = []
        sentence_cers = []
        for ref, hyp in zip(reference_sentences, hypothesis_sentences):
            wer = jiwer.wer(ref, hyp)
            cer = jiwer.cer(ref, hyp)
            sentence_wers.append(wer)
            sentence_cers.append(cer)

        if not sentence_wers or not sentence_cers:
            return {
                "sentence_wers": [],
                "sentence_cers": [],
                "average_wer": 0.0,
                "average_cer": 0.0,
                "std_dev_wer": 0.0,
                "std_dev_cer": 0.0
            }

        average_wer = np.mean(sentence_wers)
        average_cer = np.mean(sentence_cers)
        std_dev_wer = np.std(sentence_wers)
        std_dev_cer = np.std(sentence_cers)

        return {
            "sentence_wers": sentence_wers,
            "sentence_cers": sentence_cers,
            "average_wer": average_wer,
            "average_cer": average_cer,
            "std_dev_wer": std_dev_wer,
            "std_dev_cer": std_dev_cer
        }
    except Exception as e:
        raise e
        
def identify_misaligned_sentences(reference_text, hypothesis_text):
    """
    Identify sentences that don't match between reference and hypothesis.
    Returns a dictionary with misaligned sentence pairs, their indices, and misalignment details.
    """
    reference_sentences = split_into_sentences(reference_text)
    hypothesis_sentences = split_into_sentences(hypothesis_text)

    misaligned = []
    for i, (ref, hyp) in enumerate(zip(reference_sentences, hypothesis_sentences)):
        if ref != hyp:
            # Find the first position where the sentences diverge
            min_len = min(len(ref), len(hyp))
            misalignment_start = 0
            for j in range(min_len):
                if ref[j] != hyp[j]:
                    misalignment_start = j
                    break
            # Prepare the context for display
            context_ref = ref[:misalignment_start] + f"**{ref[misalignment_start:]}**"
            context_hyp = hyp[:misalignment_start] + f"**{hyp[misalignment_start:]}**"

            misaligned.append({
                "index": i+1,
                "reference": ref,
                "hypothesis": hyp,
                "misalignment_start": misalignment_start,
                "context_ref": context_ref,
                "context_hyp": context_hyp
            })

    return misaligned

def format_sentence_metrics(sentence_wers, sentence_cers, average_wer, average_cer, std_dev_wer, std_dev_cer, misaligned_sentences):
    md = "### Sentence-level Metrics\n\n"
    md += "#### Word Error Rate (WER)\n"
    md += f"* Average WER: {average_wer:.2f}\n"
    md += f"* Standard Deviation: {std_dev_wer:.2f}\n\n"
    md += "#### Character Error Rate (CER)\n"
    md += f"* Average CER: {average_cer:.2f}\n"
    md += f"* Standard Deviation: {std_dev_cer:.2f}\n\n"

    md += "### WER for Each Sentence\n\n"
    for i, wer in enumerate(sentence_wers):
        md += f"* Sentence {i+1}: {wer:.2f}\n"

    md += "\n### CER for Each Sentence\n\n"
    for i, cer in enumerate(sentence_cers):
        md += f"* Sentence {i+1}: {cer:.2f}\n"

    if misaligned_sentences:
        md += "\n### Misaligned Sentences\n\n"
        for misaligned in misaligned_sentences:
            md += f"#### Sentence {misaligned['index']}\n"
            md += f"* Reference: {misaligned['context_ref']}\n"
            md += f"* Hypothesis: {misaligned['context_hyp']}\n"
            md += f"* Misalignment starts at position: {misaligned['misalignment_start']}\n\n"

    return md

@spaces.GPU()
def process_files(reference_file, hypothesis_file):
    try:
        with open(reference_file.name, 'r') as f:
            reference_text = f.read()

        with open(hypothesis_file.name, 'r') as f:
            hypothesis_text = f.read()

        overall_wer = calculate_wer(reference_text, hypothesis_text)
        overall_cer = calculate_cer(reference_text, hypothesis_text)
        sentence_metrics = calculate_sentence_metrics(reference_text, hypothesis_text)
        misaligned = identify_misaligned_sentences(reference_text, hypothesis_text)

        return {
            "Overall WER": overall_wer,
            "Overall CER": overall_cer,
            "Sentence WERs": sentence_metrics["sentence_wers"],
            "Sentence CERs": sentence_metrics["sentence_cers"],
            "Average WER": sentence_metrics["average_wer"],
            "Average CER": sentence_metrics["average_cer"],
            "Standard Deviation WER": sentence_metrics["std_dev_wer"],
            "Standard Deviation CER": sentence_metrics["std_dev_cer"],
            "Misaligned Sentences": misaligned
        }
    except Exception as e:
        return {"error": str(e)}

def main():
    with gr.Blocks() as demo:
        gr.Markdown("# ASR Metrics")

        with gr.Row():
            reference_file = gr.File(label="Upload Reference File")
            hypothesis_file = gr.File(label="Upload Model Output File")

        with gr.Row():
            reference_preview = gr.Textbox(label="Reference Preview", lines=3)
            hypothesis_preview = gr.Textbox(label="Hypothesis Preview", lines=3)

        with gr.Row():
            compute_button = gr.Button("Compute Metrics")
            results_output = gr.JSON(label="Results")
            metrics_output = gr.Markdown(label="Sentence Metrics")
            misaligned_output = gr.Markdown(label="Misaligned Sentences")

        # Update previews when files are uploaded
        def update_previews(ref_file, hyp_file):
            ref_text = ""
            hyp_text = ""

            if ref_file:
                with open(ref_file.name, 'r') as f:
                    ref_text = f.read()[:200]  # Show first 200 characters
            if hyp_file:
                with open(hyp_file.name, 'r') as f:
                    hyp_text = f.read()[:200]  # Show first 200 characters

            return ref_text, hyp_text

        reference_file.change(
            fn=update_previews,
            inputs=[reference_file, hypothesis_file],
            outputs=[reference_preview, hypothesis_preview]
        )
        hypothesis_file.change(
            fn=update_previews,
            inputs=[reference_file, hypothesis_file],
            outputs=[reference_preview, hypothesis_preview]
        )

        def process_and_display(ref_file, hyp_file):
            result = process_files(ref_file, hyp_file)
            if "error" in result:
                error_msg = result["error"]
                return {"error": error_msg}, "", "", {"error": error_msg}

            metrics = {
                "Overall WER": result["Overall WER"],
                "Overall CER": result["Overall CER"]
            }

            metrics_md = format_sentence_metrics(
                result["Sentence WERs"],
                result["Sentence CERs"],
                result["Average WER"],
                result["Average CER"],
                result["Standard Deviation WER"],
                result["Standard Deviation CER"],
                result["Misaligned Sentences"]
            )

            misaligned_md = "### Misaligned Sentences\n\n"
            for misaligned in result["Misaligned Sentences"]:
                misaligned_md += f"#### Sentence {misaligned['index']}\n"
                misaligned_md += f"* Reference: {misaligned['context_ref']}\n"
                misaligned_md += f"* Hypothesis: {misaligned['context_hyp']}\n"
                misaligned_md += f"* Misalignment starts at position: {misaligned['misalignment_start']}\n\n"

            return metrics, metrics_md, misaligned_md

        compute_button.click(
            fn=process_and_display,
            inputs=[reference_file, hypothesis_file],
            outputs=[results_output, metrics_output, misaligned_output]
        )

    demo.launch()

if __name__ == "__main__":
    main()