Upload app.py

app.py

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+
+import gradio as gr
+import random
+import json
+import os
+from difflib import SequenceMatcher
+from jiwer import wer
+import torchaudio
+import torch
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, HubertForCTC, HubertProcessor
+import whisper
+
+# Load metadata
+with open("common_voice_en_validated_249_hf_ready.json") as f:
+    data = json.load(f)
+
+# Available filter values
+ages = sorted(set(entry["age"] for entry in data))
+genders = sorted(set(entry["gender"] for entry in data))
+accents = sorted(set(entry["accent"] for entry in data))
+
+# Load models
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Whisper
+whisper_model = whisper.load_model("medium").to(device)
+
+# Wav2Vec2
+wav2vec_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h-lv60-self")
+wav2vec_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h-lv60-self").to(device)
+
+# HuBERT
+hubert_processor = HubertProcessor.from_pretrained("facebook/hubert-large-ls960-ft")
+hubert_model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft").to(device)
+
+def load_audio(file_path):
+    waveform, sr = torchaudio.load(file_path)
+    return torchaudio.functional.resample(waveform, orig_freq=sr, new_freq=16000)[0].numpy()
+
+def transcribe_whisper(file_path):
+    result = whisper_model.transcribe(file_path)
+    return result["text"].strip().lower()
+
+def transcribe_wav2vec(file_path):
+    audio = load_audio(file_path)
+    inputs = wav2vec_processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = wav2vec_model(**inputs.to(device)).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    return wav2vec_processor.batch_decode(predicted_ids)[0].strip().lower()
+
+def transcribe_hubert(file_path):
+    audio = load_audio(file_path)
+    inputs = hubert_processor(audio, sampling_rate=16000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = hubert_model(**inputs.to(device)).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    return hubert_processor.batch_decode(predicted_ids)[0].strip().lower()
+
+def highlight_differences(ref, hyp):
+    sm = SequenceMatcher(None, ref.split(), hyp.split())
+    result = []
+    for opcode, i1, i2, j1, j2 in sm.get_opcodes():
+        if opcode == 'equal':
+            result.extend(hyp.split()[j1:j2])
+        elif opcode in ('replace', 'insert', 'delete'):
+            wrong = hyp.split()[j1:j2]
+            result.extend([f"<span style='color:red'>{w}</span>" for w in wrong])
+    return " ".join(result)
+
+def run_demo(age, gender, accent):
+    filtered = [
+        entry for entry in data
+        if entry["age"] == age and entry["gender"] == gender and entry["accent"] == accent
+    ]
+    if not filtered:
+        return "No matching sample.", None, "", "", "", "", "", ""
+
+    sample = random.choice(filtered)
+    file_path = os.path.join("common_voice_en_validated_249", sample["path"])
+    gold = sample["sentence"].strip().lower()
+
+    whisper_text = transcribe_whisper(file_path)
+    wav2vec_text = transcribe_wav2vec(file_path)
+    hubert_text = transcribe_hubert(file_path)
+
+    table = f"""
+    <table border="1" style="width:100%">
+        <tr><th>Model</th><th>Transcription</th><th>WER</th></tr>
+        <tr><td><b>Gold</b></td><td>{gold}</td><td>0.00</td></tr>
+        <tr><td>Whisper</td><td>{highlight_differences(gold, whisper_text)}</td><td>{wer(gold, whisper_text):.2f}</td></tr>
+        <tr><td>Wav2Vec2</td><td>{highlight_differences(gold, wav2vec_text)}</td><td>{wer(gold, wav2vec_text):.2f}</td></tr>
+        <tr><td>HuBERT</td><td>{highlight_differences(gold, hubert_text)}</td><td>{wer(gold, hubert_text):.2f}</td></tr>
+    </table>
+    """
+
+    return sample["sentence"], file_path, gold, whisper_text, wav2vec_text, hubert_text, table, f"Audio path: {file_path}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# ASR Model Comparison on ESL Audio")
+    gr.Markdown("Filter by age, gender, and accent. Then generate a random ESL learner's audio to compare how Whisper, Wav2Vec2, and HuBERT transcribe it.")
+
+    with gr.Row():
+        age = gr.Dropdown(choices=ages, label="Age")
+        gender = gr.Dropdown(choices=genders, label="Gender")
+        accent = gr.Dropdown(choices=accents, label="Accent")
+
+    btn = gr.Button("Generate and Transcribe")
+    audio = gr.Audio(label="Audio", type="filepath")
+    wer_output = gr.HTML()
+
+    btn.click(fn=run_demo, inputs=[age, gender, accent], outputs=[
+        gr.Textbox(label="Gold (Correct)"),
+        audio,
+        gr.Textbox(label="Whisper Output"),
+        gr.Textbox(label="Wav2Vec2 Output"),
+        gr.Textbox(label="HuBERT Output"),
+        wer_output,
+        gr.Textbox(label="Path")
+    ])
+
+demo.launch()