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import os
# Fix permission errors for Streamlit
os.environ["XDG_CACHE_HOME"] = "/tmp/.cache"
os.environ["STREAMLIT_CONFIG_DIR"] = "/tmp/streamlit/config"
os.environ["STREAMLIT_CACHE_DIR"] = "/tmp/streamlit/cache"
os.environ["STREAMLIT_STATIC_DIR"] = "/tmp/streamlit/static"
os.environ["STREAMLIT_RUNTIME_DIR"] = "/tmp/streamlit/runtime"
import requests
import tempfile
import streamlit as st
from moviepy.editor import VideoFileClip
from speechbrain.inference.classifiers import EncoderClassifier
st.set_page_config(page_title="Accent Classifier", page_icon="🗣️", layout="centered")
# Custom CSS to change background color and style input box
st.markdown(
"""
<style>
.stApp {
background-color: #f0f0f0;
}
input[type="text"] {
background-color: white;
color: black;
border: 1px solid #ccc;
padding: 0.5rem;
border-radius: 5px;
}
.stTextInput > div > div > input {
background-color: white !important;
color: black !important;
border: 1px solid #ccc !important;
}
</style>
""",
unsafe_allow_html=True
)
MODEL_ID = "Jzuluaga/accent-id-commonaccent_ecapa"
def download_video(url, output_path):
response = requests.get(url, stream=True)
if response.status_code == 200:
with open(output_path, "wb") as f:
for chunk in response.iter_content(1024):
f.write(chunk)
if not os.path.exists(output_path) or os.path.getsize(output_path) < 1024:
raise Exception("❌ Video download failed or file too small.")
else:
raise Exception("❌ Failed to download video.")
def extract_audio(video_path, audio_path):
clip = VideoFileClip(video_path)
audio = clip.audio
audio.write_audiofile(audio_path, fps=16000, nbytes=2, codec='pcm_s16le', ffmpeg_params=["-ac", "1"])
clip.close()
audio.close()
@st.cache_resource(show_spinner="Loading model...")
def load_model():
model_path = "/tmp/accent-id-model" # ✅ switch from /data to /tmp
os.makedirs(model_path, exist_ok=True)
classifier = EncoderClassifier.from_hparams(
source=MODEL_ID,
savedir=model_path
)
return classifier
def classify_accent(audio_path, classifier):
audio_path_clean = os.path.abspath(audio_path).replace('\\', '/')
if not os.path.exists(audio_path_clean):
raise FileNotFoundError(f"Audio file not found: {audio_path_clean}")
out_prob, score, index, label = classifier.classify_file(audio_path_clean)
return label, round(score.item() * 100, 2)
# ---------------- UI ----------------
st.title("🗣️ Accent Classifier from Video")
st.markdown("Paste a direct **video URL (MP4)** and then press **Enter** or click **Identify the Accent**.")
with st.form("url_form", clear_on_submit=False):
video_url = st.text_input("🔗 Video URL", placeholder="https://...")
submitted = st.form_submit_button("🗣️ Identify the Accent")
if submitted:
if not video_url:
st.warning("⚠️ Please enter a video URL.")
else:
try:
if "dropbox.com" in video_url and "raw=1" not in video_url:
video_url = video_url.replace("dl=0", "raw=1").replace("?dl=0", "?raw=1")
with st.spinner("🔄 Downloading and processing video..."):
with tempfile.TemporaryDirectory() as tmpdir:
video_path = os.path.join(tmpdir, "input_video.mp4")
audio_path = os.path.join(tmpdir, "output_audio.wav")
download_video(video_url, video_path)
extract_audio(video_path, audio_path)
classifier = load_model()
label, confidence = classify_accent(audio_path, classifier)
st.success("✅ Accent classified successfully!")
st.markdown(f"### 🎯 Prediction: **{label}**")
st.markdown(f"🧠 Confidence: **{confidence}%**")
st.info(f"The speaker's accent is predicted to be **{label}** with **{confidence}%** confidence.")
except Exception as e:
st.error(f"❌ Error: {e}")
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