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import streamlit as st |
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import os |
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from transformers import pipeline |
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from transformers.utils import logging |
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import numpy as np |
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import pandas as pd |
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import yt_dlp |
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import torchaudio |
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import ffmpeg |
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logging.set_verbosity_info() |
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RATE_HZ = 16000 |
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MAX_SECONDS = 1 |
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MAX_LENGTH = RATE_HZ * MAX_SECONDS |
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MAX_SEGMENTS = 250 |
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def download_video(url, output_path="video.mp4"): |
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ydl_opts = { |
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'format': 'worstvideo[ext=mp4]+bestaudio[ext=m4a]/bestaudio', |
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'outtmpl': output_path, |
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'merge_output_format': 'mp4', |
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'quiet': True, |
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'noplaylist': True, |
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'nocheckcertificate': True, |
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'retries': 3, |
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} |
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with yt_dlp.YoutubeDL(ydl_opts) as ydl: |
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ydl.download([url]) |
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return output_path |
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def extract_audio(input_path, output_path="audio.mp3"): |
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( |
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ffmpeg |
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.input(input_path) |
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.output(output_path, format='mp3', acodec='libmp3lame', audio_bitrate='192k') |
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.overwrite_output() |
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.run(quiet=True) |
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) |
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return output_path |
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def split_audio(file): |
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segmented_audio = [] |
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try: |
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audio, rate = torchaudio.load(str(file)) |
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transform = torchaudio.transforms.Resample(rate, RATE_HZ) |
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num_segments = (len(audio[0]) // MAX_LENGTH) |
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for i in range(num_segments): |
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if i >= MAX_SEGMENTS: |
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break |
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start = i * MAX_LENGTH |
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end = min((i + 1) * MAX_LENGTH, len(audio[0])) |
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segment = audio[0][start:end] |
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segment = transform(segment).squeeze(0).numpy().reshape(-1) |
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segmented_audio.append(segment) |
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except Exception as e: |
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print(f"Error processing file: {e}") |
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return segmented_audio |
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else: |
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return np.concatenate(segmented_audio) |
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accent_mapping = { |
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'us': 'American', |
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'canada': 'Canadian', |
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'england': 'British', |
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'indian': 'Indian', |
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'australia': 'Australian', |
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} |
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st.set_page_config(page_title="Accent Classifier", layout="centered") |
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st.title("🎙️ English Accent Classifier") |
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st.markdown("Upload a video link and get the English accent with confidence.") |
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video_url = st.text_input("Paste a public video URL (Loom, or MP4):") |
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if st.button("Analyze"): |
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if not video_url.strip(): |
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st.warning("Please enter a valid URL.") |
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else: |
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with st.spinner("Downloading video..."): |
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video_path = download_video(video_url) |
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with st.spinner("Extracting audio..."): |
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audio_path = extract_audio(video_path) |
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with st.spinner("Extracting Waves..."): |
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waves = split_audio(audio_path) |
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with st.spinner("Classifying accent..."): |
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model_name = "dima806/english_accents_classification" |
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pipe = pipeline('audio-classification', model=model_name, device=0) |
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accent_data = pipe(waves)[0] |
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accent = accent_mapping.get(accent_data.get("label", "us")) |
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confidence = accent_data.get("score", 0) |
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st.success("Analysis Complete!") |
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st.markdown(f"**Accent:** {accent}") |
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st.markdown(f"**Confidence Score:** {confidence:.2f}%") |
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os.remove(video_path) |
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os.remove(audio_path) |
