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import gradio as gr
import random
import json
import os
from difflib import SequenceMatcher
from jiwer import wer
import torchaudio
from transformers import pipeline
# 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 pipelines
device = 0 # 0 for CUDA/GPU, -1 for CPU
pipe_whisper = pipeline("automatic-speech-recognition", model="openai/whisper-medium", device=device)
pipe_wav2vec2 = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h", device=device)
pipe_hubert = pipeline("automatic-speech-recognition", model="facebook/hubert-base-ls960", device=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(pipe, file_path):
result = pipe(file_path)
return result["text"].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(pipe_whisper, file_path)
wav2vec_text = transcribe(pipe_wav2vec2, file_path)
hubert_text = transcribe(pipe_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() |