remove token

app.py

@@ -0,0 +1,117 @@
+import os
+import sys
+import subprocess
+import tempfile
+import requests
+from moviepy.editor import VideoFileClip
+
+# Ensure the official OpenAI Whisper package is installed (supports load_model)
+try:
+    import whisper
+    if not hasattr(whisper, 'load_model'):
+        raise ImportError
+except ImportError:
+    subprocess.run([sys.executable, "-m", "pip", "install", "--upgrade", "openai-whisper"], check=True)
+    import whisper
+
+import torch
+import librosa
+import pandas as pd
+from transformers import Wav2Vec2Processor, Wav2Vec2ForSequenceClassification
+from huggingface_hub import login
+import gradio as gr
+
+# Authenticate with Hugging Face (token via HF_TOKEN env var)
+
+
+
+# Device setup (GPU if available)
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+def load_models():
+    # Load Whisper directly on the target device
+    whisper_model = whisper.load_model('base', device=device)
+    processor = Wav2Vec2Processor.from_pretrained(
+        'jonatasgrosman/wav2vec2-large-xlsr-53-english'
+    )
+    accent_model = Wav2Vec2ForSequenceClassification.from_pretrained(
+        'jonatasgrosman/wav2vec2-large-xlsr-53-english'
+    ).to(device)
+    return whisper_model, processor, accent_model
+
+whisper_model, processor, accent_model = load_models()
+
+# Main analysis function
+def analyze(video_url: str):
+    # Download video to temp file
+    with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp_vid:
+        response = requests.get(video_url, stream=True)
+        response.raise_for_status()
+        for chunk in response.iter_content(chunk_size=1024 * 1024):
+            if chunk:
+                tmp_vid.write(chunk)
+        video_path = tmp_vid.name
+
+    # Extract audio
+    audio_path = video_path.replace('.mp4', '.wav')
+    clip = VideoFileClip(video_path)
+    clip.audio.write_audiofile(audio_path, verbose=False, logger=None)
+    clip.close()
+
+    # Load audio waveform
+    speech, sr = librosa.load(audio_path, sr=16000)
+
+    # Transcribe with Whisper (model on correct device)
+    result = whisper_model.transcribe(speech)
+    transcript = result.get('text', '')
+    lang = result.get('language', 'unknown')
+    if lang != 'en':
+        transcript = f"[Non-English detected: {lang}]\n" + transcript
+
+        # Accent classification
+    inputs = processor(speech, sampling_rate=sr, return_tensors='pt', padding=True)
+    input_values = inputs.input_values.to(device)
+    attention_mask = inputs.attention_mask.to(device)
+    with torch.no_grad():
+        logits = accent_model(input_values=input_values, attention_mask=attention_mask).logits
+        probs = torch.softmax(logits, dim=-1).squeeze().cpu().tolist()
+
+    # Map default LABEL_x to human-readable accents
+    accent_labels = [
+        'American', 'Australian', 'British', 'Canadian', 'Indian',
+        'Irish', 'New Zealander', 'South African', 'Welsh'
+    ]  # ensure this matches model output order
+    accent_probs = [(accent_labels[i], probs[i] * 100) for i in range(len(probs))]
+    accent_probs.sort(key=lambda x: x[1], reverse=True)
+    top_accent, top_conf = accent_probs[0]
+
+    # Prepare DataFrame
+    df = pd.DataFrame(accent_probs, columns=['Accent', 'Confidence (%)'])
+    df = pd.DataFrame(accent_probs, columns=['Accent', 'Confidence (%)'])
+
+    # Cleanup temp files
+    try:
+        os.remove(video_path)
+        os.remove(audio_path)
+    except:
+        pass
+
+    return top_accent, f"{top_conf:.2f}%", df
+
+# Gradio interface
+interface = gr.Interface(
+    fn=analyze,
+    inputs=gr.Textbox(label='Video URL', placeholder='Enter public MP4 URL'),
+    outputs=[
+        # gr.Textbox(label='Transcript'),
+        gr.Textbox(label='Predicted Accent'),
+        gr.Textbox(label='Accent Confidence'),
+        gr.Dataframe(label='All Accent Probabilities')
+    ],
+    title='English Accent Detector',
+    description='Paste a Loom or direct MP4 URL to extract, transcribe, and classify English accents (uses GPU if available).',
+    allow_flagging='never'
+)
+
+if __name__ == '__main__':
+    interface.launch()

requirements.txt

@@ -0,0 +1,9 @@
+openai-whisper
+gradio
+requests
+moviepy
+torch
+librosa
+pandas
+transformers
+huggingface-hub