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import os
os.environ["STREAMLIT_WATCHER_TYPE"] = "none"
import streamlit as st
import tempfile
import requests
import subprocess
import torchaudio
from speechbrain.pretrained.interfaces import foreign_class
# Load model using custom interface
@st.cache_resource
def load_model():
try:
os.environ["SPEECHBRAIN_CACHE"] = os.path.join(os.getcwd(), "models")
return foreign_class(
source="Jzuluaga/accent-id-commonaccent_xlsr-en-english",
pymodule_file="custom_interface.py",
classname="CustomEncoderWav2vec2Classifier"
)
except Exception as e:
st.error(f"❌ Model failed to load: {e}")
raise
# Download video from a public URL
def download_video(url, temp_dir):
video_path = os.path.join(temp_dir, "video.mp4")
r = requests.get(url, stream=True)
with open(video_path, 'wb') as f:
for chunk in r.iter_content(chunk_size=1024):
f.write(chunk)
return video_path
import imageio_ffmpeg
def extract_audio(video_path, temp_dir):
audio_path = os.path.join(temp_dir, "audio.wav")
ffmpeg_path = imageio_ffmpeg.get_ffmpeg_exe() # Get bundled FFmpeg path
command = [
ffmpeg_path,
"-y", "-i", video_path,
"-vn", "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1",
audio_path
]
try:
subprocess.run(command, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
except subprocess.CalledProcessError as e:
raise RuntimeError(f"FFmpeg failed: {e}")
return audio_path
def classify_accent(audio_path, model):
out_prob, score, index, label = model.classify_file(audio_path)
return label, score * 100, out_prob
# Streamlit UI
st.set_page_config(page_title="Accent Classifier", layout="centered")
st.title("English Accent Detection")
st.markdown("Paste a link or upload a video to analyze the speaker's English accent.")
video_url = st.text_input("Paste a direct link to a video (MP4 URL)")
st.markdown("**OR**")
uploaded_file = st.file_uploader("Upload a video file (MP4 format)", type=["mp4"])
if uploaded_file or video_url:
with st.spinner("Processing video..."):
try:
with tempfile.TemporaryDirectory() as temp_dir:
# Get video path from upload or URL
if uploaded_file:
video_path = os.path.join(temp_dir, uploaded_file.name)
with open(video_path, 'wb') as f:
f.write(uploaded_file.read())
else:
video_path = download_video(video_url, temp_dir)
audio_path = extract_audio(video_path, temp_dir)
model = load_model()
label, confidence, probs = classify_accent(audio_path, model)
# Ensure proper formatting
label = label if isinstance(label, str) else label[0]
st.success(f"Detected Accent: **{label}**")
st.info(f"Confidence Score: **{confidence.item():.1f}%**")
except Exception as e:
st.error(f"❌ Error: {str(e)}")
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