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import os
import tempfile
import streamlit as st
import soundfile as sf
import librosa
from yt_dlp import YoutubeDL
from moviepy.editor import VideoFileClip
import whisper
import whisper.tokenizer as tok
from speechbrain.pretrained import EncoderClassifier
import numpy as np
from audio_recorder_streamlit import audio_recorder
# βββββββββββββββββββββββββββββββββββββββββββββββ
# 1) Page config & Dark Theme Styling
# βββββββββββββββββββββββββββββββββββββββββββββββ
st.set_page_config(page_title="English & Accent Detector", page_icon="π€", layout="wide")
st.markdown("""
<style>
body, .stApp { background-color: #121212; color: #e0e0e0; overflow-y: scroll; }
.stButton>button {
background-color: #1f77b4; color: #fff;
border-radius:8px; padding:0.6em 1.2em; font-size:1rem;
}
.stButton>button:hover { background-color: #105b88; }
.stVideo > video { max-width: 300px !important; border: 1px solid #333; }
</style>
""", unsafe_allow_html=True)
# βββββββββββββββββββββββββββββββββββββββββββββββ
# 2) Load models once
# βββββββββββββββββββββββββββββββββββββββββββββββ
wmodel = whisper.load_model("tiny")
classifier = EncoderClassifier.from_hparams(
source="Jzuluaga/accent-id-commonaccent_ecapa",
savedir="pretrained_models/accent-id-commonaccent_ecapa"
)
# βββββββββββββββββββββββββββββββββββββββββββββββ
# 3) Accent grouping map
# βββββββββββββββββββββββββββββββββββββββββββββββ
GROUP_MAP = {
"england": "British", "us": "American", "canada": "American",
"australia": "Australian", "newzealand": "Australian",
"indian": "Indian", "scotland": "Scottish", "ireland": "Irish",
"wales": "Welsh", "african": "African", "malaysia": "Malaysian",
"bermuda": "Bermudian", "philippines": "Philippine",
"hongkong": "Hong Kong", "singapore": "Singaporean",
"southatlandtic": "Other"
}
def group_accents(raw_list):
return [(GROUP_MAP.get(r, r.capitalize()), p) for r, p in raw_list]
# βββββββββββββββββββββββββββββββββββββββββββββββ
# 4) Helper functions
# βββββββββββββββββββββββββββββββββββββββββββββββ
def download_extract_audio(url, out_vid="clip.mp4", out_wav="clip.wav",
max_duration=60, sr=16000):
if os.path.exists(out_vid): os.remove(out_vid)
with YoutubeDL({"outtmpl": out_vid, "merge_output_format": "mp4"}) as ydl:
ydl.download([url])
clip = VideoFileClip(out_vid)
used = min(clip.duration, max_duration)
sub = clip.subclip(0, used)
sub.audio.write_audiofile(out_wav, fps=sr, codec="pcm_s16le")
clip.close(); sub.close()
wav, rate = librosa.load(out_wav, sr=sr, mono=True)
return wav, rate, out_wav, out_vid
def detect_language_whisper(wav_path):
audio = whisper.load_audio(wav_path, sr=16000)
audio = whisper.pad_or_trim(audio)
mel = whisper.log_mel_spectrogram(audio).to(wmodel.device)
_, probs = wmodel.detect_language(mel)
lang = max(probs, key=probs.get)
conf = probs.get("en", 0.0) * 100
return lang, conf
def classify_clip_topk(wav_path, k=3):
out_prob, _, _, _ = classifier.classify_file(wav_path)
probs = out_prob.squeeze().cpu().numpy()
idxs = probs.argsort()[-k:][::-1]
return [(classifier.hparams.label_encoder.ind2lab[i], float(probs[i]))
for i in idxs]
# βββββββββββββββββββββββββββββββββββββββββββββββ
# 5) Streamlit UI
# βββββββββββββββββββββββββββββββββββββββββββββββ
st.title("π€ English & Accent Detector")
st.write("""
This tool helps you determine if a speaker is speaking English and identifies their accent.
π§ **How to use:**
- Use **URL** for public **YouTube**, **Loom**, or any **MP4-accessible video link**.
- Use **Upload** to submit local video files (MP4, MOV, WEBM, MKV).
- Use **Record** to record short audio snippets directly from your browser microphone.
""")
st.sidebar.header("π₯ Input")
method = st.sidebar.radio("Input method", ["URL", "Upload", "Record"])
url = None
uploaded = None
audio_bytes = None
if method == "URL":
url = st.sidebar.text_input("Video URL (e.g. YouTube, Loom, MP4 link)")
elif method == "Upload":
uploaded = st.sidebar.file_uploader("Upload a video file", type=["mp4", "mov", "webm", "mkv"])
elif method == "Record":
st.sidebar.write("ποΈ Click below to start recording (wait for microphone access prompt):")
audio_bytes = audio_recorder()
if not audio_bytes:
st.sidebar.info("Waiting for you to record your voice...")
else:
st.sidebar.success("Audio recorded successfully! You can now classify it.")
if st.sidebar.button("Classify Accent"):
with st.spinner("π Extracting audio..."):
if method == "URL" and url:
wav, sr, wav_path, vid_path = download_extract_audio(url)
elif method == "Upload" and uploaded:
vid_path = tempfile.NamedTemporaryFile(
suffix=os.path.splitext(uploaded.name)[1], delete=False
).name
with open(vid_path, "wb") as f:
f.write(uploaded.read())
clip = VideoFileClip(vid_path)
wav_path = "clip.wav"
clip.audio.write_audiofile(wav_path, fps=16000, codec="pcm_s16le")
clip.close()
wav, sr = librosa.load(wav_path, sr=16000, mono=True)
elif method == "Record" and audio_bytes:
wav_path = "recorded.wav"
with open(wav_path, "wb") as f:
f.write(audio_bytes)
wav, sr = librosa.load(wav_path, sr=16000, mono=True)
vid_path = None
else:
st.error("Please supply a valid input.")
st.stop()
left, right = st.columns([1, 2])
with left:
st.subheader("πΊ Preview")
if method == "Record":
st.audio(audio_bytes, format="audio/wav")
elif vid_path:
with open(vid_path, "rb") as f:
st.video(f.read())
with right:
with st.spinner("π Detecting English..."):
lang_code, eng_conf = detect_language_whisper(wav_path)
if eng_conf >= 4.0:
st.markdown(
"<div style='background-color:#1b5e20; color:#a5d6a7; padding:8px;"
" border-radius:5px;'>β
<strong>English detected β classifying accent...</strong></div>",
unsafe_allow_html=True
)
with st.spinner("π― Classifying accent..."):
raw3 = classify_clip_topk(wav_path, k=3)
grouped = group_accents(raw3)
st.subheader("π£οΈ Accent Classification")
cols = st.columns(len(grouped))
for c, (lbl, p) in zip(cols, grouped):
c.markdown(
f"""<div style=\"border:1px solid #444; border-radius:8px; padding:15px; text-align:center;\">
<div style=\"font-size:1.1em; font-weight:bold; color:#90caf9\">{lbl}</div>
<div style=\"font-size:1.8em; color:#29b6f6;\">{p*100:5.1f}%</div>
</div>""",
unsafe_allow_html=True
)
else:
st.markdown(
"<div style='background-color:#b71c1c; color:#ffcdd2; padding:8px;"
" border-radius:5px;'>β <strong>English not detected</strong></div>",
unsafe_allow_html=True
)
name = tok.LANGUAGES.get(lang_code, lang_code).capitalize()
st.write(f"**Top detected language:** {name} ({eng_conf:.1f}% English)")
for p in (wav_path, vid_path):
if p and os.path.exists(p):
try:
os.remove(p)
except:
pass
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