Create app.py

app.py

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+import streamlit as st
+import whisper
+import tempfile
+import os
+import re
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# =========================
+# PAGE CONFIG
+# =========================
+st.set_page_config(page_title="Speech to Text Translator", page_icon="🎙️", layout="centered")
+
+# =========================
+# UI STYLE
+# =========================
+st.markdown("""
+<style>
+html, body, [data-testid="stAppViewContainer"] {
+    background: radial-gradient(circle at 20% 20%, #1e293b, #020617 70%);
+    color: white;
+    font-family: 'Inter', sans-serif;
+}
+.title {
+    text-align: center;
+    font-size: 40px;
+    font-weight: 700;
+}
+.subtitle {
+    text-align: center;
+    color: #cbd5e1;
+    margin-bottom: 22px;
+}
+[data-testid="stFileUploader"] {
+    border-radius: 16px;
+    border: 1px dashed rgba(255,255,255,0.25);
+}
+.result-box {
+    background: rgba(16,185,129,0.12);
+    border-radius: 16px;
+    padding: 16px;
+    border: 1px solid rgba(16,185,129,0.35);
+}
+</style>
+""", unsafe_allow_html=True)
+
+# =========================
+# HEADER
+# =========================
+st.markdown('<div class="title">🎙️ Speech to Text Translator</div>', unsafe_allow_html=True)
+st.markdown('<div class="subtitle">Transcribe speech or translate into any language</div>', unsafe_allow_html=True)
+
+# =========================
+# TEXT UTILS
+# =========================
+def split_text(text, max_len=200):
+    sentences = re.split(r'(?<=[.!?。！？])', text)
+    chunks = []
+    cur = ""
+    for s in sentences:
+        if len(cur) + len(s) < max_len:
+            cur += " " + s
+        else:
+            chunks.append(cur.strip())
+            cur = s
+    if cur:
+        chunks.append(cur.strip())
+    return chunks
+
+# =========================
+# MODEL CACHE
+# =========================
+@st.cache_resource
+def load_models():
+    whisper_model = whisper.load_model("base")
+    model_name = "facebook/nllb-200-distilled-600M"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    nllb_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    return whisper_model, tokenizer, nllb_model
+
+whisper_model, tokenizer, nllb_model = load_models()
+
+# =========================
+# LANGUAGE MAP
+# =========================
+LANG_CODE = {
+    "Arabic":"arb_Arab","Assamese":"asm_Beng","Awadhi":"awa_Deva",
+    "Bengali":"ben_Beng","Bhojpuri":"bho_Deva","Chinese":"zho_Hans",
+    "English":"eng_Latn","French":"fra_Latn","German":"deu_Latn",
+    "Hindi":"hin_Deva","Japanese":"jpn_Jpan","Korean":"kor_Hang",
+    "Maithili":"mai_Deva","Marathi":"mar_Deva","Persian":"pes_Arab",
+    "Punjabi":"pan_Guru","Russian":"rus_Cyrl","Sanskrit":"san_Deva",
+    "Spanish":"spa_Latn","Tamil":"tam_Taml","Telugu":"tel_Telu",
+    "Urdu":"urd_Arab","Vietnamese":"vie_Latn"
+}
+
+# =========================
+# OPTIONS
+# =========================
+col1, col2 = st.columns(2)
+with col1:
+    transcribe = st.checkbox("📄 Transcribe")
+with col2:
+    translate = st.checkbox("🌍 Translate", value=True)
+
+target_lang = None
+if translate:
+    target_lang = st.selectbox("Translate into", sorted(LANG_CODE.keys()))
+
+# =========================
+# UPLOAD
+# =========================
+audio_file = st.file_uploader(
+    "Upload audio (MP3, WAV, M4A, MP4)",
+    type=["mp3","wav","m4a","mp4"]
+)
+
+if audio_file:
+    st.audio(audio_file)
+
+# =========================
+# PROCESS
+# =========================
+if st.button("🚀 Process Audio") and audio_file:
+
+    with st.spinner("Processing audio..."):
+
+        # Save uploaded audio
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
+            tmp.write(audio_file.read())
+            tmp_path = tmp.name
+
+        # ---------- TRANSCRIBE ----------
+        result = whisper_model.transcribe(tmp_path, fp16=False)
+        detected_lang = result["language"]
+        text = result["text"]
+
+        st.success(f"Detected language: {detected_lang}")
+
+        output_text = text
+
+        # ---------- TRANSLATE ----------
+        if translate and target_lang:
+            tgt_code = LANG_CODE[target_lang]
+            chunks = split_text(text)
+
+            translated_parts = []
+
+            for chunk in chunks:
+                inputs = tokenizer(chunk, return_tensors="pt")
+
+                tokens = nllb_model.generate(
+                    **inputs,
+                    forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_code),
+                    max_length=256,
+                    num_beams=4,
+                    no_repeat_ngram_size=3,
+                    repetition_penalty=1.2,
+                    early_stopping=True
+                )
+
+                translated = tokenizer.batch_decode(tokens, skip_special_tokens=True)[0]
+                translated_parts.append(translated)
+
+            output_text = " ".join(translated_parts)
+
+        # ================= OUTPUT =================
+        st.markdown("### 📝 Output Text")
+        st.markdown(f'<div class="result-box">{output_text}</div>', unsafe_allow_html=True)
+
+        st.download_button(
+            "⬇ Download Text",
+            output_text,
+            file_name="translated_text.txt"
+        )
+
+        os.remove(tmp_path)