Upload app.py

app.py

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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
+
+# ───────────────────────────────────────────────
+# 1) Page config & Dark Theme Styling
+# ───────────────────────────────────────────────
+st.set_page_config(page_title="English & Accent Detector", page_icon="🎤", 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)
+
+# ───────────────────────────────────────────────
+# 2) Load models once
+# ───────────────────────────────────────────────
+wmodel = whisper.load_model("tiny")
+classifier = EncoderClassifier.from_hparams(
+    source="Jzuluaga/accent-id-commonaccent_ecapa",
+    savedir="pretrained_models/accent-id-commonaccent_ecapa"
+)
+
+# ───────────────────────────────────────────────
+# 3) Accent grouping map
+# ───────────────────────────────────────────────
+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]
+
+# ───────────────────────────────────────────────
+# 4) Helper functions
+# ───────────────────────────────────────────────
+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]
+
+# ───────────────────────────────────────────────
+# 5) Streamlit UI
+# ───────────────────────────────────────────────
+st.title("🎤 English & Accent Detector")
+st.write("""
+    This tool helps you determine if a speaker is speaking English and identifies their accent.
+    
+    🧭 **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("📥 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("🎙️ 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("🔊 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("📺 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("🔎 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;'>✅ <strong>English detected – classifying accent...</strong></div>",
+                unsafe_allow_html=True
+            )
+            with st.spinner("🎯 Classifying accent..."):
+                raw3 = classify_clip_topk(wav_path, k=3)
+            grouped = group_accents(raw3)
+
+            st.subheader("🗣️ 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;'>❌ <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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