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README.md

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+---
+title: PlotWeaver - Live Commentary Translation
+emoji: "\U0001F3DF\uFE0F"
+colorFrom: green
+colorTo: yellow
+sdk: gradio
+sdk_version: "5.50.0"
+app_file: app.py
+pinned: true
+license: mit
+hardware: t4-small
+models:
+  - PlotweaverAI/whisper-small-de-en
+  - PlotweaverAI/nllb-200-distilled-600M-african-6lang
+  - PlotweaverAI/yoruba-mms-tts-new
+tags:
+  - speech-to-speech
+  - translation
+  - dubbing
+  - multi-language
+  - football
+  - commentary
+  - streaming
+short_description: Translate live English commentary to 40+ languages with AI
+---

app.py

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+"""
+PlotWeaver — Live Commentary Translation Platform (Single File)
+================================================================
+Three engines: Qwen Omni | YourVoic API | Local (Whisper+NLLB+MMS-TTS)
+"""
+
+import os, io, re, time, base64, struct, shutil, subprocess, tempfile, logging
+import torch, numpy as np, requests, soundfile as sf, gradio as gr
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+logger = logging.getLogger(__name__)
+
+
+# =============================================================================
+# LANGUAGES
+# =============================================================================
+
+# Qwen Omni voices (work across all Qwen-supported languages)
+QWEN_VOICES = [
+    "Cherry", "Serena", "Ethan", "Chelsie", "Momo", "Vivian", "Moon", "Maia",
+    "Kai", "Nofish", "Bella", "Jennifer", "Ryan", "Katerina", "Aiden",
+    "Eldric Sage", "Mia", "Mochi", "Bellona", "Vincent", "Bunny", "Neil",
+    "Elias", "Arthur", "Seren", "Bodega", "Sonrisa", "Alek", "Dolce",
+    "Sohee", "Ono Anna", "Lenn", "Emilien", "Andre",
+]
+
+# Each language entry:
+#   "Display Name": {
+#       "nllb": NLLB-200 language code (for local/yourvoic pipeline translation),
+#       "yourvoic_lang": YourVoic language code (or None),
+#       "yourvoic_voices": list of YourVoic voice names,
+#       "tts_engine": "qwen" | "yourvoic" | "local",
+#       "qwen_code": short language code for Qwen prompts (or None),
+#       "qwen_name": full language name for Qwen system prompt (or None),
+#   }
+
+LANGUAGES = {
+    # ---- Global Languages (Qwen Omni — best quality) ----
+    "Arabic": {
+        "nllb": "arb_Arab", "yourvoic_lang": "ar-SA",
+        "yourvoic_voices": ["Peter"], "tts_engine": "qwen",
+        "qwen_code": "ar", "qwen_name": "Modern Standard Arabic (العربية الفصحى)",
+    },
+    "Spanish": {
+        "nllb": "spa_Latn", "yourvoic_lang": "es-ES",
+        "yourvoic_voices": ["Peter", "Kylie"], "tts_engine": "qwen",
+        "qwen_code": "es", "qwen_name": "Spanish",
+    },
+    "French": {
+        "nllb": "fra_Latn", "yourvoic_lang": "fr-FR",
+        "yourvoic_voices": ["Peter", "Kylie"], "tts_engine": "qwen",
+        "qwen_code": "fr", "qwen_name": "French",
+    },
+    "German": {
+        "nllb": "deu_Latn", "yourvoic_lang": "de-DE",
+        "yourvoic_voices": ["Peter", "Kylie"], "tts_engine": "qwen",
+        "qwen_code": "de", "qwen_name": "German",
+    },
+    "Mandarin": {
+        "nllb": "zho_Hans", "yourvoic_lang": "zh-CN",
+        "yourvoic_voices": ["Peter", "Kylie"], "tts_engine": "qwen",
+        "qwen_code": "zh", "qwen_name": "Mandarin Chinese",
+    },
+    "Italian": {
+        "nllb": "ita_Latn", "yourvoic_lang": "it-IT",
+        "yourvoic_voices": ["Peter", "Kylie"], "tts_engine": "qwen",
+        "qwen_code": "it", "qwen_name": "Italian",
+    },
+    "Japanese": {
+        "nllb": "jpn_Jpan", "yourvoic_lang": "ja-JP",
+        "yourvoic_voices": ["Peter", "Kylie"], "tts_engine": "qwen",
+        "qwen_code": "ja", "qwen_name": "Japanese",
+    },
+    "Portuguese": {
+        "nllb": "por_Latn", "yourvoic_lang": "pt-BR",
+        "yourvoic_voices": ["Peter", "Kylie"], "tts_engine": "qwen",
+        "qwen_code": "pt", "qwen_name": "Portuguese",
+    },
+    "Hindi": {
+        "nllb": "hin_Deva", "yourvoic_lang": "hi-IN",
+        "yourvoic_voices": ["Rahul", "Deepika", "Aditya"], "tts_engine": "qwen",
+        "qwen_code": "hi", "qwen_name": "Hindi",
+    },
+    "Korean": {
+        "nllb": "kor_Hang", "yourvoic_lang": "ko-KR",
+        "yourvoic_voices": ["Peter", "Kylie"], "tts_engine": "qwen",
+        "qwen_code": "ko", "qwen_name": "Korean",
+    },
+    "Russian": {
+        "nllb": "rus_Cyrl", "yourvoic_lang": "ru-RU",
+        "yourvoic_voices": ["Peter", "Kylie"], "tts_engine": "qwen",
+        "qwen_code": "ru", "qwen_name": "Russian",
+    },
+
+    # ---- African Languages (Local pipeline: Whisper → NLLB → MMS-TTS) ----
+    "Yoruba": {
+        "nllb": "yor_Latn", "yourvoic_lang": None,
+        "yourvoic_voices": [], "tts_engine": "local",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Hausa": {
+        "nllb": "hau_Latn", "yourvoic_lang": None,
+        "yourvoic_voices": [], "tts_engine": "local",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Igbo": {
+        "nllb": "ibo_Latn", "yourvoic_lang": None,
+        "yourvoic_voices": [], "tts_engine": "local",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Swahili": {
+        "nllb": "swh_Latn", "yourvoic_lang": "sw-KE",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Zulu": {
+        "nllb": "zul_Latn", "yourvoic_lang": None,
+        "yourvoic_voices": [], "tts_engine": "local",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Amharic": {
+        "nllb": "amh_Ethi", "yourvoic_lang": "am-ET",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Afrikaans": {
+        "nllb": "afr_Latn", "yourvoic_lang": "af-ZA",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+
+    # ---- South Asian (YourVoic TTS + NLLB MT) ----
+    "Bengali": {
+        "nllb": "ben_Beng", "yourvoic_lang": "bn-IN",
+        "yourvoic_voices": ["Sneha", "Aryan"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Tamil": {
+        "nllb": "tam_Taml", "yourvoic_lang": "ta-IN",
+        "yourvoic_voices": ["Priya", "Kumar"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Telugu": {
+        "nllb": "tel_Telu", "yourvoic_lang": "te-IN",
+        "yourvoic_voices": ["Arjun", "Lakshmi"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Marathi": {
+        "nllb": "mar_Deva", "yourvoic_lang": "mr-IN",
+        "yourvoic_voices": ["Anjali", "Rohan"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Urdu": {
+        "nllb": "urd_Arab", "yourvoic_lang": "ur-PK",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Nepali": {
+        "nllb": "npi_Deva", "yourvoic_lang": "ne-NP",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+
+    # ---- Southeast Asian (YourVoic) ----
+    "Indonesian": {
+        "nllb": "ind_Latn", "yourvoic_lang": "id-ID",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Vietnamese": {
+        "nllb": "vie_Latn", "yourvoic_lang": "vi-VN",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Thai": {
+        "nllb": "tha_Thai", "yourvoic_lang": "th-TH",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Malay": {
+        "nllb": "zsm_Latn", "yourvoic_lang": "ms-MY",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Filipino": {
+        "nllb": "tgl_Latn", "yourvoic_lang": "fil-PH",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+
+    # ---- European (YourVoic) ----
+    "Dutch": {
+        "nllb": "nld_Latn", "yourvoic_lang": "nl-NL",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Polish": {
+        "nllb": "pol_Latn", "yourvoic_lang": "pl-PL",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Turkish": {
+        "nllb": "tur_Latn", "yourvoic_lang": "tr-TR",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Swedish": {
+        "nllb": "swe_Latn", "yourvoic_lang": "sv-SE",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Romanian": {
+        "nllb": "ron_Latn", "yourvoic_lang": "ro-RO",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Greek": {
+        "nllb": "ell_Grek", "yourvoic_lang": "el-GR",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Ukrainian": {
+        "nllb": "ukr_Cyrl", "yourvoic_lang": "uk-UA",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Finnish": {
+        "nllb": "fin_Latn", "yourvoic_lang": "fi-FI",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Danish": {
+        "nllb": "dan_Latn", "yourvoic_lang": "da-DK",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Norwegian": {
+        "nllb": "nob_Latn", "yourvoic_lang": "nb-NO",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+
+    # ---- Middle Eastern (YourVoic) ----
+    "Persian": {
+        "nllb": "pes_Arab", "yourvoic_lang": "fa-IR",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+    "Hebrew": {
+        "nllb": "heb_Hebr", "yourvoic_lang": "he-IL",
+        "yourvoic_voices": ["Peter"], "tts_engine": "yourvoic",
+        "qwen_code": None, "qwen_name": None,
+    },
+}
+
+
+# Group languages by category for the UI
+LANGUAGE_GROUPS = {
+    "Global Languages": [
+        "Spanish", "French", "German", "Mandarin", "Italian",
+        "Japanese", "Portuguese", "Hindi", "Arabic", "Korean", "Russian",
+    ],
+    "African Languages": [
+        "Yoruba", "Hausa", "Igbo", "Swahili", "Zulu", "Amharic", "Afrikaans",
+    ],
+    "South Asian": [
+        "Bengali", "Tamil", "Telugu", "Marathi", "Urdu", "Nepali",
+    ],
+    "Southeast Asian": [
+        "Indonesian", "Vietnamese", "Thai", "Malay", "Filipino",
+    ],
+    "European": [
+        "Dutch", "Polish", "Turkish", "Swedish", "Romanian",
+        "Greek", "Ukrainian", "Finnish", "Danish", "Norwegian",
+    ],
+    "Middle Eastern": [
+        "Persian", "Hebrew",
+    ],
+}
+
+# All language display names (for dropdowns)
+ALL_LANGUAGE_NAMES = sorted(LANGUAGES.keys())
+
+# Languages that use local TTS (your fine-tuned models)
+LOCAL_TTS_LANGUAGES = [k for k, v in LANGUAGES.items() if v["tts_engine"] == "local"]
+
+# Languages that use YourVoic API
+YOURVOIC_LANGUAGES = [k for k, v in LANGUAGES.items() if v["tts_engine"] == "yourvoic"]
+
+
+# =============================================================================
+# PIPELINE: ASR + MT + Video helpers
+# =============================================================================
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+TORCH_DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32
+
+# Models (loaded once at startup)
+asr_pipe = None
+mt_tokenizer = None
+mt_model = None
+tts_pipe_local = None  # Local TTS for Yoruba/Hausa/Igbo/Zulu
+
+
+def load_models():
+    """Load all models at startup."""
+    global asr_pipe, mt_tokenizer, mt_model, tts_pipe_local
+    from transformers import (
+        pipeline as hf_pipeline,
+        AutoTokenizer,
+        AutoModelForSeq2SeqLM,
+    )
+
+    print(f"Device: {DEVICE} | Dtype: {TORCH_DTYPE}")
+    print("Loading models...")
+
+    # ASR
+    ASR_MODEL_ID = "PlotweaverAI/whisper-small-de-en"
+    print(f"  Loading ASR: {ASR_MODEL_ID}")
+    asr_pipe = hf_pipeline(
+        "automatic-speech-recognition",
+        model=ASR_MODEL_ID,
+        device=DEVICE,
+        torch_dtype=TORCH_DTYPE,
+    )
+    print("  ASR loaded")
+
+    # MT
+    MT_MODEL_ID = "PlotweaverAI/nllb-200-distilled-600M-african-6lang"
+    print(f"  Loading MT: {MT_MODEL_ID}")
+    mt_tokenizer = AutoTokenizer.from_pretrained(MT_MODEL_ID)
+    mt_model = AutoModelForSeq2SeqLM.from_pretrained(
+        MT_MODEL_ID, torch_dtype=TORCH_DTYPE
+    ).to(DEVICE)
+    mt_tokenizer.src_lang = "eng_Latn"
+    print("  MT loaded")
+
+    # Local TTS (Yoruba)
+    TTS_MODEL_ID = "PlotweaverAI/yoruba-mms-tts-new"
+    print(f"  Loading local TTS: {TTS_MODEL_ID}")
+    tts_pipe_local = hf_pipeline(
+        "text-to-speech",
+        model=TTS_MODEL_ID,
+        device=DEVICE,
+        torch_dtype=TORCH_DTYPE,
+    )
+    print("  Local TTS loaded")
+
+    # Diagnostics
+    print(f"\n=== Device diagnostics ===")
+    print(f"CUDA available: {torch.cuda.is_available()}")
+    if torch.cuda.is_available():
+        print(f"CUDA device: {torch.cuda.get_device_name(0)}")
+    print(f"ASR on: {next(asr_pipe.model.parameters()).device}")
+    print(f"MT on: {next(mt_model.parameters()).device}")
+    print(f"TTS on: {next(tts_pipe_local.model.parameters()).device}")
+    print(f"YourVoic API key: {'set' if os.environ.get('YOURVOIC_API_KEY') else 'NOT SET'}")
+    print(f"==========================\n")
+    print("All models loaded!")
+
+
+# ---- Text Processing ----
+
+def split_into_sentences(text):
+    """Split raw ASR text into individual sentences."""
+    text = text.strip()
+    if not text:
+        return []
+    text = '. '.join(s.strip().capitalize() for s in text.split('. ') if s.strip())
+    if re.search(r'[.!?]', text):
+        sentences = re.split(r'(?<=[.!?])\s+', text)
+        return [s.strip() for s in sentences if s.strip()]
+    words = text.split()
+    MAX_WORDS = 12
+    sentences = []
+    for i in range(0, len(words), MAX_WORDS):
+        chunk = ' '.join(words[i:i + MAX_WORDS])
+        if not chunk.endswith(('.', '!', '?')):
+            chunk += '.'
+        chunk = chunk[0].upper() + chunk[1:] if len(chunk) > 1 else chunk.upper()
+        sentences.append(chunk)
+    return sentences
+
+
+# ---- ASR ----
+
+def transcribe(audio_array, sample_rate=16000):
+    """ASR: English audio to text. Handles both short and long audio."""
+    if len(audio_array) < 1600:
+        return ""
+
+    duration_s = len(audio_array) / sample_rate
+
+    if sample_rate != 16000:
+        import torchaudio.functional as F_audio
+        audio_tensor = torch.from_numpy(audio_array).float()
+        audio_tensor = F_audio.resample(audio_tensor, sample_rate, 16000)
+        audio_array = audio_tensor.numpy()
+        sample_rate = 16000
+
+    if duration_s <= 28:
+        result = asr_pipe(
+            {"raw": audio_array, "sampling_rate": sample_rate},
+            return_timestamps=False,
+        )
+        return result["text"].strip()
+
+    # Long-form: native Whisper generate
+    model = asr_pipe.model
+    processor = asr_pipe.feature_extractor
+    tokenizer = asr_pipe.tokenizer
+
+    inputs = processor(
+        audio_array, sampling_rate=16000, return_tensors="pt",
+        truncation=False, padding="longest", return_attention_mask=True,
+    )
+    input_features = inputs.input_features.to(DEVICE, dtype=TORCH_DTYPE)
+    attention_mask = inputs.attention_mask.to(DEVICE) if "attention_mask" in inputs else None
+
+    generate_kwargs = {"return_timestamps": True, "language": "en", "task": "transcribe"}
+    if attention_mask is not None:
+        generate_kwargs["attention_mask"] = attention_mask
+
+    with torch.no_grad():
+        predicted_ids = model.generate(input_features, **generate_kwargs)
+
+    transcription = tokenizer.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+    return transcription.strip()
+
+
+# ---- MT ----
+
+def translate_sentence(text, target_nllb_code, fast=True, max_length=256):
+    """Translate a single sentence from English to target language."""
+    inputs = mt_tokenizer(text, return_tensors="pt", truncation=True).to(DEVICE)
+    tgt_lang_id = mt_tokenizer.convert_tokens_to_ids(target_nllb_code)
+
+    generate_kwargs = {
+        "forced_bos_token_id": tgt_lang_id,
+        "repetition_penalty": 1.5,
+        "no_repeat_ngram_size": 3,
+    }
+    if fast:
+        generate_kwargs.update({"max_length": 128, "num_beams": 1, "do_sample": False})
+    else:
+        generate_kwargs.update({"max_length": max_length, "num_beams": 4, "early_stopping": True})
+
+    with torch.no_grad():
+        output_ids = mt_model.generate(**inputs, **generate_kwargs)
+
+    return mt_tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+
+def translate_text(text, target_nllb_code, fast=True):
+    """Split and translate full text sentence-by-sentence."""
+    sentences = split_into_sentences(text)
+    if not sentences:
+        return "", [], []
+    translations = []
+    for s in sentences:
+        yo = translate_sentence(s, target_nllb_code, fast=fast)
+        translations.append(yo)
+    return ' '.join(translations), sentences, translations
+
+
+# ---- Video Processing ----
+
+def extract_audio_from_video(video_path, output_path, target_sr=16000):
+    """Extract audio track from video as 16kHz mono WAV."""
+    cmd = [
+        "ffmpeg", "-y", "-i", video_path,
+        "-vn", "-acodec", "pcm_s16le", "-ar", str(target_sr), "-ac", "1",
+        output_path,
+    ]
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    if result.returncode != 0:
+        raise RuntimeError(f"ffmpeg extraction failed: {result.stderr[:200]}")
+    return output_path
+
+
+def get_media_duration(path):
+    """Get duration in seconds."""
+    cmd = [
+        "ffprobe", "-v", "error",
+        "-show_entries", "format=duration",
+        "-of", "default=noprint_wrappers=1:nokey=1", path,
+    ]
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    if result.returncode != 0:
+        raise RuntimeError(f"ffprobe failed: {result.stderr[:200]}")
+    return float(result.stdout.strip())
+
+
+def stretch_audio_to_duration(input_path, output_path, target_duration_s):
+    """Stretch/compress audio to match target duration."""
+    current_duration = get_media_duration(input_path)
+    if current_duration <= 0:
+        raise RuntimeError("Invalid audio duration")
+
+    ratio = current_duration / target_duration_s
+    filters = []
+    remaining = ratio
+    while remaining > 2.0:
+        filters.append("atempo=2.0")
+        remaining /= 2.0
+    while remaining < 0.5:
+        filters.append("atempo=0.5")
+        remaining /= 0.5
+    filters.append(f"atempo={remaining:.4f}")
+
+    cmd = ["ffmpeg", "-y", "-i", input_path, "-filter:a", ",".join(filters), output_path]
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    if result.returncode != 0:
+        raise RuntimeError(f"ffmpeg tempo failed: {result.stderr[:200]}")
+    return output_path
+
+
+def mux_video_audio(video_path, audio_path, output_path, extend_video=False, target_duration=None):
+    """Combine video with new audio. Optionally extend video by freezing last frame."""
+    if extend_video and target_duration:
+        cmd = [
+            "ffmpeg", "-y", "-i", video_path, "-i", audio_path,
+            "-filter_complex", f"[0:v]tpad=stop_mode=clone:stop_duration={target_duration}[v]",
+            "-map", "[v]", "-map", "1:a:0",
+            "-c:v", "libx264", "-preset", "fast", "-c:a", "aac",
+            "-t", str(target_duration), output_path,
+        ]
+    else:
+        cmd = [
+            "ffmpeg", "-y", "-i", video_path, "-i", audio_path,
+            "-c:v", "copy", "-c:a", "aac",
+            "-map", "0:v:0", "-map", "1:a:0", "-shortest", output_path,
+        ]
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    if result.returncode != 0:
+        raise RuntimeError(f"ffmpeg mux failed: {result.stderr[:200]}")
+    return output_path
+
+
+# =============================================================================
+# TTS ENGINE: YourVoic API + Local MMS-TTS
+# =============================================================================
+
+YOURVOIC_API_KEY = os.environ.get("YOURVOIC_API_KEY", "")
+YOURVOIC_STREAM_URL = "https://yourvoic.com/api/v1/tts/stream"
+
+
+def synthesize_yourvoic(text, language_code, voice="Peter", speed=1.0):
+    """
+    Synthesize text using YourVoic API.
+    Returns (audio_array, sample_rate) or raises on failure.
+    """
+    if not YOURVOIC_API_KEY:
+        raise RuntimeError(
+            "YOURVOIC_API_KEY not set. Add it as a Space secret."
+        )
+
+    headers = {
+        "X-API-Key": YOURVOIC_API_KEY,
+        "Content-Type": "application/json",
+    }
+    payload = {
+        "text": text,
+        "voice": voice,
+        "language": language_code,
+        "model": "aura-prime",
+        "speed": speed,
+    }
+
+    t0 = time.time()
+    response = requests.post(
+        YOURVOIC_STREAM_URL,
+        headers=headers,
+        json=payload,
+        stream=True,
+        timeout=60,
+    )
+
+    if response.status_code != 200:
+        raise RuntimeError(
+            f"YourVoic API error {response.status_code}: {response.text[:200]}"
+        )
+
+    # Collect streamed audio bytes into a temp file
+    import tempfile
+    tmp_raw = tempfile.NamedTemporaryFile(suffix=".audio", delete=False)
+    for chunk in response.iter_content(chunk_size=8192):
+        tmp_raw.write(chunk)
+    tmp_raw.close()
+
+    elapsed = time.time() - t0
+    logger.info(f"YourVoic TTS: {len(text)} chars, {elapsed:.2f}s")
+
+    # Try reading directly with soundfile
+    try:
+        audio_array, sample_rate = sf.read(tmp_raw.name, dtype="float32")
+        os.unlink(tmp_raw.name)
+        return audio_array, sample_rate
+    except Exception as e:
+        logger.warning(f"soundfile can't read YourVoic output directly: {e}")
+
+    # Fallback: convert with ffmpeg to WAV
+    try:
+        import subprocess
+        tmp_wav = tmp_raw.name + ".wav"
+        result = subprocess.run(
+            ["ffmpeg", "-y", "-i", tmp_raw.name,
+             "-acodec", "pcm_s16le", "-ar", "24000", "-ac", "1", tmp_wav],
+            capture_output=True, text=True,
+        )
+        os.unlink(tmp_raw.name)
+        if result.returncode != 0:
+            raise RuntimeError(f"ffmpeg conversion failed: {result.stderr[:200]}")
+        audio_array, sample_rate = sf.read(tmp_wav, dtype="float32")
+        os.unlink(tmp_wav)
+        return audio_array, sample_rate
+    except Exception as e2:
+        # Clean up
+        for f in [tmp_raw.name, tmp_raw.name + ".wav"]:
+            if os.path.exists(f):
+                os.unlink(f)
+        raise RuntimeError(f"Failed to decode YourVoic audio: {e2}")
+
+
+def synthesize_yourvoic_to_file(text, language_code, output_path, voice="Peter", speed=1.0):
+    """Synthesize via YourVoic and save to file."""
+    audio, sr = synthesize_yourvoic(text, language_code, voice, speed)
+    sf.write(output_path, audio, sr)
+    return output_path, sr
+
+
+def synthesize_local(text, tts_pipe):
+    """
+    Synthesize text using local HuggingFace TTS pipeline (MMS-TTS).
+    Returns (audio_array, sample_rate).
+    """
+    t0 = time.time()
+    result = tts_pipe(text)
+    audio = np.array(result["audio"]).squeeze()
+    sr = result["sampling_rate"]
+    elapsed = time.time() - t0
+    logger.info(f"Local TTS: {len(text)} chars, {elapsed:.2f}s, {len(audio)/sr:.1f}s audio")
+    return audio, sr
+
+
+def synthesize_chunked(text, language_config, tts_pipe=None, sentences_per_chunk=2):
+    """
+    Synthesize long text by chunking into sentence groups.
+    Routes to either YourVoic or local TTS based on language config.
+
+    Args:
+        text: Full text to synthesize
+        language_config: Dict from LANGUAGES (has tts_engine, yourvoic_lang, etc.)
+        tts_pipe: Local HuggingFace TTS pipeline (needed for local engine)
+        sentences_per_chunk: How many sentences to synthesize per API call
+
+    Returns:
+        (audio_array, sample_rate)
+    """
+    import re
+    sentences = re.split(r'(?<=[.!?])\s+', text)
+    sentences = [s.strip() for s in sentences if s.strip()]
+
+    if not sentences:
+        return np.array([], dtype=np.float32), 16000
+
+    engine = language_config["tts_engine"]
+    audio_segments = []
+    output_sr = None
+
+    for i in range(0, len(sentences), sentences_per_chunk):
+        chunk_text = ' '.join(sentences[i:i + sentences_per_chunk])
+        if not chunk_text:
+            continue
+
+        try:
+            if engine == "yourvoic":
+                voice = language_config["yourvoic_voices"][0] if language_config["yourvoic_voices"] else "Peter"
+                lang_code = language_config["yourvoic_lang"]
+                audio_seg, seg_sr = synthesize_yourvoic(chunk_text, lang_code, voice)
+            else:
+                if tts_pipe is None:
+                    raise RuntimeError("Local TTS pipeline not loaded")
+                audio_seg, seg_sr = synthesize_local(chunk_text, tts_pipe)
+
+            if output_sr is None:
+                output_sr = seg_sr
+            if len(audio_seg) > 0:
+                audio_segments.append(audio_seg)
+                # Small silence between chunks
+                silence = np.zeros(int(0.15 * seg_sr), dtype=np.float32)
+                audio_segments.append(silence)
+
+        except Exception as e:
+            logger.error(f"TTS chunk failed: {e}")
+            continue
+
+    if not audio_segments:
+        # Return a short silence instead of empty array to prevent Gradio crash
+        fallback_sr = output_sr or 16000
+        silence = np.zeros(int(0.5 * fallback_sr), dtype=np.float32)
+        logger.warning("All TTS chunks failed — returning silence")
+        return silence, fallback_sr
+
+    return np.concatenate(audio_segments), output_sr
+
+
+# =============================================================================
+# QWEN OMNI ENGINE
+# =============================================================================
+
+QWEN_MODEL = "qwen3.5-omni-plus"
+QWEN_BASE_URL = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
+
+
+def _get_client():
+    """Create OpenAI-compatible client for Qwen Dashscope API."""
+    from openai import OpenAI
+    api_key = os.environ.get("DASHSCOPE_API_KEY", "")
+    if not api_key:
+        raise RuntimeError(
+            "DASHSCOPE_API_KEY not set. Add it as a Space secret."
+        )
+    return OpenAI(api_key=api_key, base_url=QWEN_BASE_URL)
+
+
+def _wav_to_base64(wav_path):
+    """Read WAV file and return base64 string."""
+    with open(wav_path, "rb") as f:
+        return base64.b64encode(f.read()).decode("utf-8")
+
+
+def _base64_to_wav(b64_data, output_path):
+    """Convert raw PCM base64 audio to WAV file (24kHz, mono, 16-bit)."""
+    audio_bytes = base64.b64decode(b64_data)
+    sample_rate = 24000
+    num_channels = 1
+    bits_per_sample = 16
+    byte_rate = sample_rate * num_channels * bits_per_sample // 8
+    block_align = num_channels * bits_per_sample // 8
+    data_size = len(audio_bytes)
+    with open(output_path, "wb") as f:
+        f.write(b"RIFF")
+        f.write(struct.pack("<I", 36 + data_size))
+        f.write(b"WAVE")
+        f.write(b"fmt ")
+        f.write(struct.pack("<I", 16))
+        f.write(struct.pack("<H", 1))
+        f.write(struct.pack("<H", num_channels))
+        f.write(struct.pack("<I", sample_rate))
+        f.write(struct.pack("<I", byte_rate))
+        f.write(struct.pack("<H", block_align))
+        f.write(struct.pack("<H", bits_per_sample))
+        f.write(b"data")
+        f.write(struct.pack("<I", data_size))
+        f.write(audio_bytes)
+
+
+def _extract_audio_chunk(video_path, output_wav, start_sec, duration_sec):
+    """Extract a chunk of audio from video as 16kHz mono WAV."""
+    subprocess.run(
+        ["ffmpeg", "-y", "-ss", str(start_sec), "-t", str(duration_sec),
+         "-i", video_path, "-vn", "-acodec", "pcm_s16le",
+         "-ar", "16000", "-ac", "1", output_wav],
+        capture_output=True, check=True,
+    )
+
+
+def _get_duration(filepath):
+    """Get media file duration in seconds."""
+    result = subprocess.run(
+        ["ffprobe", "-v", "quiet", "-show_entries", "format=duration",
+         "-of", "default=noprint_wrappers=1:nokey=1", filepath],
+        capture_output=True, text=True,
+    )
+    return float(result.stdout.strip())
+
+
+def _concatenate_wavs(wav_files, output_path):
+    """Concatenate WAV files using ffmpeg."""
+    if len(wav_files) == 1:
+        shutil.copy2(wav_files[0], output_path)
+        return
+    list_file = output_path + ".txt"
+    with open(list_file, "w") as f:
+        for wav in wav_files:
+            f.write(f"file '{wav}'\n")
+    subprocess.run(
+        ["ffmpeg", "-y", "-f", "concat", "-safe", "0",
+         "-i", list_file, "-c", "copy", output_path],
+        capture_output=True, check=True,
+    )
+    os.remove(list_file)
+
+
+def _build_system_prompt(language_name):
+    """Build Qwen system prompt for a target language."""
+    return (
+        f"You are a professional video dubbing translator. You will receive audio in English.\n"
+        f"Your task:\n"
+        f"1. Listen carefully to the English speech.\n"
+        f"2. Translate it into natural, fluent {language_name}.\n"
+        f"3. Respond ONLY with the {language_name} translation spoken aloud — no English, no commentary,\n"
+        f"   no meta-text, no transliteration. Speak entirely in {language_name}.\n"
+        f"4. Match the tone, emotion, and pacing of the original speaker as closely as possible.\n"
+        f"5. If there are pauses or silence in the original audio, maintain similar pacing.\n"
+        f"6. Translate idioms and cultural references into their {language_name} equivalents.\n"
+        f"7. Use clear, professional pronunciation suitable for a broad audience."
+    )
+
+
+def translate_chunk_qwen(wav_path, voice, language_name, chunk_index=0):
+    """
+    Translate a single audio chunk using Qwen Omni.
+    
+    Args:
+        wav_path: Path to input WAV file (English audio)
+        voice: Qwen voice name (e.g. "Ethan", "Cherry")
+        language_name: Full language name for the system prompt
+        chunk_index: For logging
+    
+    Returns:
+        (output_wav_path, transcript) or (None, transcript) if no audio
+    """
+    client = _get_client()
+    audio_b64 = _wav_to_base64(wav_path)
+    output_wav = wav_path.replace(".wav", f"_qwen_{chunk_index}.wav")
+
+    system_prompt = _build_system_prompt(language_name)
+    user_prompt = f"Translate this English speech into {language_name}. Respond only with the spoken {language_name} translation."
+
+    t0 = time.time()
+    completion = client.chat.completions.create(
+        model=QWEN_MODEL,
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "input_audio",
+                        "input_audio": {
+                            "data": f"data:audio/wav;base64,{audio_b64}",
+                            "format": "wav",
+                        },
+                    },
+                    {"type": "text", "text": user_prompt},
+                ],
+            },
+        ],
+        modalities=["text", "audio"],
+        audio={"voice": voice, "format": "wav"},
+        stream=True,
+        stream_options={"include_usage": True},
+    )
+
+    audio_chunks = []
+    transcript_parts = []
+
+    for event in completion:
+        if not event.choices:
+            continue
+        delta = event.choices[0].delta
+        if hasattr(delta, "content") and delta.content:
+            transcript_parts.append(delta.content)
+        if hasattr(delta, "audio") and delta.audio:
+            if isinstance(delta.audio, dict):
+                if "data" in delta.audio:
+                    audio_chunks.append(delta.audio["data"])
+            elif hasattr(delta.audio, "data") and delta.audio.data:
+                audio_chunks.append(delta.audio.data)
+
+    transcript = "".join(transcript_parts)
+    elapsed = time.time() - t0
+    logger.info(f"Qwen chunk {chunk_index}: {elapsed:.1f}s, transcript={transcript[:60]}")
+
+    if audio_chunks:
+        full_audio_b64 = "".join(audio_chunks)
+        _base64_to_wav(full_audio_b64, output_wav)
+        return output_wav, transcript
+
+    return None, transcript
+
+
+def dub_video_qwen(video_path, language_name, voice="Ethan", chunk_seconds=120, progress_fn=None):
+    """
+    Full video dubbing pipeline using Qwen Omni.
+    Splits video into chunks, translates each chunk via Qwen API,
+    concatenates results, and muxes back onto video.
+    
+    Args:
+        video_path: Path to input video
+        language_name: Full language name (e.g. "French", "Arabic")
+        voice: Qwen voice name
+        chunk_seconds: Audio chunk duration for API calls
+        progress_fn: Optional gradio progress callback
+    
+    Returns:
+        (output_video_path, log_text)
+    """
+    tmp_dir = tempfile.mkdtemp(prefix=f"qwen_dub_")
+    log = []
+
+    try:
+        # Duration
+        if progress_fn:
+            progress_fn(0.05, desc="Analyzing video...")
+        total_duration = _get_duration(video_path)
+        log.append(f"**Video:** {total_duration:.1f}s")
+        log.append(f"**Engine:** Qwen 3.5 Omni")
+        log.append(f"**Voice:** {voice}")
+        log.append(f"**Language:** {language_name}")
+
+        if total_duration > 3600:
+            return None, "Video longer than 1 hour — please use a shorter clip."
+
+        # Split into chunks
+        if progress_fn:
+            progress_fn(0.1, desc="Extracting audio chunks...")
+        num_chunks = max(1, int(total_duration // chunk_seconds) + (1 if total_duration % chunk_seconds > 0 else 0))
+        log.append(f"**Chunks:** {num_chunks} ({chunk_seconds}s each)")
+
+        input_chunks = []
+        for i in range(num_chunks):
+            start = i * chunk_seconds
+            duration = min(chunk_seconds, total_duration - start)
+            chunk_path = os.path.join(tmp_dir, f"chunk_{i:03d}.wav")
+            _extract_audio_chunk(video_path, chunk_path, start, duration)
+            input_chunks.append(chunk_path)
+
+        # Translate each chunk
+        output_chunks = []
+        all_transcripts = []
+
+        for i, chunk_path in enumerate(input_chunks):
+            if progress_fn:
+                frac = 0.15 + 0.7 * (i / num_chunks)
+                progress_fn(frac, desc=f"Translating chunk {i+1}/{num_chunks}...")
+
+            result_path, transcript = translate_chunk_qwen(
+                chunk_path, voice, language_name, i
+            )
+            if transcript:
+                all_transcripts.append(f"**[{i+1}]** {transcript}")
+
+            if result_path:
+                output_chunks.append(result_path)
+            else:
+                # Silence fallback
+                duration = _get_duration(chunk_path)
+                silence_path = os.path.join(tmp_dir, f"silence_{i:03d}.wav")
+                subprocess.run(
+                    ["ffmpeg", "-y", "-f", "lavfi",
+                     "-i", "anullsrc=r=24000:cl=mono",
+                     "-t", str(duration), "-acodec", "pcm_s16le", silence_path],
+                    capture_output=True, check=True,
+                )
+                output_chunks.append(silence_path)
+
+        # Concatenate
+        if progress_fn:
+            progress_fn(0.88, desc="Assembling audio...")
+        full_audio = os.path.join(tmp_dir, "full_dubbed.wav")
+        _concatenate_wavs(output_chunks, full_audio)
+
+        # Mux onto video
+        if progress_fn:
+            progress_fn(0.93, desc="Combining audio and video...")
+        output_video = os.path.join(tmp_dir, "dubbed_output.mp4")
+        subprocess.run(
+            ["ffmpeg", "-y", "-i", video_path, "-i", full_audio,
+             "-c:v", "copy", "-map", "0:v:0", "-map", "1:a:0",
+             "-shortest", output_video],
+            capture_output=True, check=True,
+        )
+
+        if progress_fn:
+            progress_fn(1.0, desc="Done!")
+
+        log.append(f"\n**Transcript:**")
+        log.extend(all_transcripts)
+
+        return output_video, "\n".join(log)
+
+    except Exception as e:
+        logger.exception("Qwen dubbing failed")
+        shutil.rmtree(tmp_dir, ignore_errors=True)
+        return None, f"Error: {str(e)}"
+
+
+# =============================================================================
+# GRADIO APP
+# =============================================================================
+
+# Load models at startup
+load_models()
+
+# =============================================================================
+# Helper functions
+# =============================================================================
+
+def get_voices_for_language(lang_name):
+    """Get available voices for a language based on its engine."""
+    config = LANGUAGES.get(lang_name, {})
+    engine = config.get("tts_engine", "local")
+    if engine == "qwen":
+        return QWEN_VOICES
+    elif engine == "yourvoic" and config.get("yourvoic_voices"):
+        return config["yourvoic_voices"]
+    elif engine == "local":
+        return ["Default (local model)"]
+    return ["Peter"]
+
+
+def full_pipeline_audio(audio_input, target_language):
+    """Full pipeline: English audio → target language audio."""
+    if audio_input is None:
+        return None, "Please upload or record audio."
+
+    lang_config = LANGUAGES.get(target_language)
+    if not lang_config:
+        return None, f"Language '{target_language}' not configured."
+
+    sample_rate, audio_array = audio_input
+    audio_array = audio_array.astype(np.float32)
+    if audio_array.ndim > 1:
+        audio_array = audio_array.mean(axis=1)
+    if audio_array.max() > 1.0 or audio_array.min() < -1.0:
+        max_val = max(abs(audio_array.max()), abs(audio_array.min()))
+        if max_val > 0:
+            audio_array = audio_array / max_val
+
+    log = []
+    total_start = time.time()
+
+    # ASR
+    t0 = time.time()
+    english = transcribe(audio_array, sample_rate)
+    log.append(f"**ASR** ({time.time()-t0:.2f}s)\n{english}")
+    if not english:
+        return None, "ASR returned empty text."
+
+    # MT
+    t0 = time.time()
+    nllb_code = lang_config["nllb"]
+    translated, en_sents, tgt_sents = translate_text(english, nllb_code, fast=False)
+    log.append(f"\n**Translation** ({time.time()-t0:.2f}s)")
+    for e, t in zip(en_sents, tgt_sents):
+        log.append(f"  EN: {e}\n  {target_language.upper()}: {t}")
+    if not translated:
+        return None, "Translation returned empty."
+
+    # TTS
+    t0 = time.time()
+    audio_out, sr_out = synthesize_chunked(
+        translated, lang_config, tts_pipe=tts_pipe_local
+    )
+    log.append(f"\n**TTS** ({time.time()-t0:.2f}s) = {len(audio_out)/sr_out:.1f}s audio")
+
+    total = time.time() - total_start
+    log.append(f"\n**Total: {total:.2f}s**")
+
+    return (sr_out, audio_out), "\n".join(log)
+
+
+def full_pipeline_text(english_text, target_language, voice_name):
+    """Text-only pipeline: English text → target language audio."""
+    if not english_text or not english_text.strip():
+        return None, "Please enter English text."
+
+    lang_config = LANGUAGES.get(target_language)
+    if not lang_config:
+        return None, f"Language '{target_language}' not configured."
+
+    log = []
+    total_start = time.time()
+
+    # MT
+    t0 = time.time()
+    nllb_code = lang_config["nllb"]
+    translated, en_sents, tgt_sents = translate_text(english_text.strip(), nllb_code, fast=False)
+    log.append(f"**Translation** ({time.time()-t0:.2f}s)")
+    for e, t in zip(en_sents, tgt_sents):
+        log.append(f"  EN: {e}\n  {target_language.upper()}: {t}")
+    if not translated:
+        return None, "Translation returned empty."
+
+    # TTS
+    t0 = time.time()
+    audio_out, sr_out = synthesize_chunked(
+        translated, lang_config, tts_pipe=tts_pipe_local
+    )
+    log.append(f"\n**TTS** ({time.time()-t0:.2f}s) = {len(audio_out)/sr_out:.1f}s audio")
+
+    total = time.time() - total_start
+    log.append(f"\n**Total: {total:.2f}s**")
+
+    return (sr_out, audio_out), "\n".join(log)
+
+
+def dub_video(video_path, target_languages, dub_voice, chunk_seconds, progress=gr.Progress()):
+    """
+    Dub a video into one or more target languages.
+    Routes to Qwen Omni for global languages, local pipeline for African languages.
+    """
+    if video_path is None:
+        return None, "Please upload a video."
+
+    if not target_languages:
+        return None, "Please select at least one target language."
+
+    results_log = []
+    output_videos = []
+
+    for lang_name in target_languages:
+        lang_config = LANGUAGES.get(lang_name)
+        if not lang_config:
+            results_log.append(f"**{lang_name}**: not configured, skipped")
+            continue
+
+        engine = lang_config.get("tts_engine", "local")
+        results_log.append(f"\n{'='*50}")
+        results_log.append(f"**Dubbing: {lang_name}** (engine: {engine})")
+        results_log.append(f"{'='*50}")
+
+        try:
+            if engine == "qwen":
+                # Qwen Omni: end-to-end speech-to-speech (best for global languages)
+                qwen_lang_name = lang_config.get("qwen_name", lang_name)
+                voice = dub_voice if dub_voice in QWEN_VOICES else "Ethan"
+                out_video, log_text = dub_video_qwen(
+                    video_path, qwen_lang_name, voice=voice,
+                    chunk_seconds=chunk_seconds, progress_fn=progress,
+                )
+                results_log.append(log_text)
+                if out_video:
+                    output_videos.append(out_video)
+
+            else:
+                # Local/YourVoic pipeline: ASR → NLLB → TTS
+                work_dir = tempfile.mkdtemp(prefix=f"dub_{lang_name}_")
+                extracted_audio = os.path.join(work_dir, "audio.wav")
+                tgt_audio_raw = os.path.join(work_dir, "tgt_raw.wav")
+                tgt_audio_aligned = os.path.join(work_dir, "tgt_aligned.wav")
+                output_video = os.path.join(work_dir, f"dubbed_{lang_name}.mp4")
+
+                progress(0.05, desc=f"{lang_name}: extracting audio...")
+                extract_audio_from_video(video_path, extracted_audio)
+                video_duration = get_media_duration(video_path)
+                results_log.append(f"Video: {video_duration:.1f}s")
+
+                audio_array, sr = sf.read(extracted_audio, dtype="float32")
+                if audio_array.ndim > 1:
+                    audio_array = audio_array.mean(axis=1)
+
+                progress(0.15, desc=f"{lang_name}: transcribing...")
+                t0 = time.time()
+                english = transcribe(audio_array, sr)
+                results_log.append(f"ASR: {time.time()-t0:.1f}s")
+                if not english:
+                    results_log.append("ASR empty — skipped")
+                    continue
+
+                progress(0.4, desc=f"{lang_name}: translating...")
+                t0 = time.time()
+                nllb_code = lang_config["nllb"]
+                translated, _, _ = translate_text(english, nllb_code, fast=True)
+                results_log.append(f"MT: {time.time()-t0:.1f}s")
+                if not translated:
+                    results_log.append("Translation empty — skipped")
+                    continue
+
+                progress(0.65, desc=f"{lang_name}: synthesizing...")
+                t0 = time.time()
+                tgt_audio, tgt_sr = synthesize_chunked(
+                    translated, lang_config, tts_pipe=tts_pipe_local
+                )
+                sf.write(tgt_audio_raw, tgt_audio, tgt_sr)
+                tgt_duration = len(tgt_audio) / tgt_sr
+                results_log.append(f"TTS: {time.time()-t0:.1f}s ({tgt_duration:.1f}s audio)")
+
+                progress(0.85, desc=f"{lang_name}: aligning...")
+                MAX_STRETCH = 1.2
+                stretch_ratio = tgt_duration / video_duration
+
+                if stretch_ratio <= MAX_STRETCH:
+                    if abs(stretch_ratio - 1.0) > 0.02:
+                        stretch_audio_to_duration(tgt_audio_raw, tgt_audio_aligned, video_duration)
+                    else:
+                        import shutil
+                        shutil.copy(tgt_audio_raw, tgt_audio_aligned)
+                    extend_video = False
+                    final_duration = video_duration
+                else:
+                    shutil.copy(tgt_audio_raw, tgt_audio_aligned)
+                    extend_video = True
+                    final_duration = tgt_duration
+                    results_log.append(f"Audio longer ({stretch_ratio:.1f}x) — extending video")
+
+                progress(0.95, desc=f"{lang_name}: combining...")
+                mux_video_audio(
+                    video_path, tgt_audio_aligned, output_video,
+                    extend_video=extend_video, target_duration=final_duration
+                )
+                output_videos.append(output_video)
+
+        except Exception as e:
+            logger.exception(f"Dubbing {lang_name} failed")
+            results_log.append(f"Error: {str(e)}")
+
+    progress(1.0, desc="Done!")
+    final_video = output_videos[0] if output_videos else None
+    return final_video, "\n".join(results_log)
+
+
+def update_voices(language):
+    """Update voice dropdown when language changes."""
+    voices = get_voices_for_language(language)
+    return gr.update(choices=voices, value=voices[0])
+
+
+# =============================================================================
+# Gradio UI
+# =============================================================================
+
+EXAMPLES = [
+    "And it's a brilliant goal from the striker!",
+    "The referee has shown a yellow card. Corner kick for the home team.",
+    "What a save by the goalkeeper! The match is heading into injury time.",
+    "He dribbles past two defenders and shoots! The ball hits the back of the net!",
+]
+
+CSS = """
+.main-header { text-align: center; margin-bottom: 0.5rem; }
+.main-header h1 { font-size: 1.8rem; font-weight: 700; margin: 0; }
+.main-header p { color: #666; font-size: 0.95rem; }
+.lang-group-label { font-weight: 600; font-size: 0.85rem; color: #888; text-transform: uppercase; letter-spacing: 0.05em; margin-top: 0.5rem; }
+"""
+
+with gr.Blocks(
+    title="PlotWeaver — Live Commentary Translation",
+    theme=gr.themes.Soft(),
+    css=CSS,
+) as demo:
+
+    gr.HTML("""
+    <div class="main-header">
+        <h1>PlotWeaver</h1>
+        <p>Live commentary translation platform &mdash; English to 40+ languages</p>
+        <p style="font-size:0.8rem; color:#999">ASR (Whisper) &rarr; MT (NLLB-200) &rarr; TTS (YourVoic + local models)</p>
+    </div>
+    """)
+
+    with gr.Tabs():
+
+        # ====== TAB 1: EVENT MANAGEMENT ======
+        with gr.TabItem("Event Management"):
+            gr.Markdown("### Create new event")
+            gr.Markdown("Configure your live broadcast event with target languages and input source.")
+
+            with gr.Row():
+                with gr.Column(scale=2):
+                    event_name = gr.Textbox(
+                        label="Event name",
+                        placeholder="e.g. Premier League: Arsenal vs. Chelsea",
+                    )
+                    with gr.Row():
+                        start_time = gr.Textbox(label="Start time", placeholder="08:30 PM")
+                        end_time = gr.Textbox(label="End time", placeholder="10:30 PM")
+                        event_date = gr.Textbox(label="Date", placeholder="2026-06-06")
+
+                    gr.Markdown("#### Input source")
+                    input_method = gr.Radio(
+                        choices=["RTMP Stream", "WebRTC (Browser)", "Direct Audio Feed"],
+                        value="RTMP Stream",
+                        label="Input method",
+                    )
+
+                    gr.Markdown("#### Target languages")
+                    gr.Markdown("Select languages for simultaneous broadcast. Additional languages consume more stream minutes.")
+
+                    # Language checkboxes grouped by category
+                    target_langs = gr.CheckboxGroup(
+                        choices=ALL_LANGUAGE_NAMES,
+                        label="Languages",
+                        value=["Yoruba"],
+                    )
+
+                with gr.Column(scale=1):
+                    gr.Markdown("#### Estimate summary")
+                    estimate_display = gr.Markdown(
+                        value="**Event:** Not configured\n\n**Languages:** 1 selected\n\n**Estimated duration:** --\n\n**Total estimate:** --"
+                    )
+                    create_event_btn = gr.Button("Create Event", variant="primary", size="lg")
+                    event_status = gr.Markdown("")
+
+            def update_estimate(name, langs, start, end):
+                n_langs = len(langs) if langs else 0
+                lang_list = ", ".join(langs) if langs else "None"
+                return (
+                    f"**Event:** {name or 'Not set'}\n\n"
+                    f"**Languages:** {n_langs} selected\n\n"
+                    f"{lang_list}\n\n"
+                    f"**Input:** Configured\n\n"
+                    f"**Rate:** 1x (Standard)"
+                )
+
+            for inp in [event_name, target_langs, start_time, end_time]:
+                inp.change(
+                    fn=update_estimate,
+                    inputs=[event_name, target_langs, start_time, end_time],
+                    outputs=[estimate_display],
+                )
+
+            def create_event(name, langs):
+                if not name:
+                    return "Please enter an event name."
+                if not langs:
+                    return "Please select at least one language."
+                return f"Event **{name}** created with {len(langs)} languages: {', '.join(langs)}"
+
+            create_event_btn.click(
+                fn=create_event,
+                inputs=[event_name, target_langs],
+                outputs=[event_status],
+            )
+
+        # ====== TAB 2: LIVE STUDIO ======
+        with gr.TabItem("Live Studio"):
+            gr.Markdown("### Live streaming translation")
+            gr.Markdown("Record or stream English commentary and hear it translated in real-time.")
+
+            with gr.Row():
+                studio_language = gr.Dropdown(
+                    choices=ALL_LANGUAGE_NAMES,
+                    value="Yoruba",
+                    label="Target language",
+                )
+                studio_voice = gr.Dropdown(
+                    choices=get_voices_for_language("Yoruba"),
+                    value=get_voices_for_language("Yoruba")[0],
+                    label="Voice",
+                )
+
+            studio_language.change(
+                fn=update_voices,
+                inputs=[studio_language],
+                outputs=[studio_voice],
+            )
+
+            with gr.Row():
+                with gr.Column():
+                    studio_audio_in = gr.Audio(
+                        label="English commentary (upload or record)",
+                        type="numpy",
+                        sources=["upload", "microphone"],
+                    )
+                    studio_translate_btn = gr.Button("Translate", variant="primary", size="lg")
+
+                with gr.Column():
+                    studio_audio_out = gr.Audio(label="Translated audio", type="numpy", autoplay=True)
+                    studio_log = gr.Markdown(label="Pipeline log")
+
+            studio_translate_btn.click(
+                fn=full_pipeline_audio,
+                inputs=[studio_audio_in, studio_language],
+                outputs=[studio_audio_out, studio_log],
+            )
+
+        # ====== TAB 3: VIDEO DUBBING ======
+        with gr.TabItem("Video Dubbing"):
+            gr.Markdown("### Video dubbing (English → multi-language)")
+            gr.Markdown(
+                "Upload a video with English commentary and get back a dubbed version. "
+                "**Global languages** (Arabic, French, Spanish, etc.) use Qwen Omni for best quality. "
+                "**African languages** (Yoruba, Hausa, etc.) use the local Whisper → NLLB → MMS-TTS pipeline."
+            )
+
+            with gr.Row():
+                with gr.Column():
+                    dub_video_in = gr.Video(label="Upload English video", sources=["upload"])
+                    dub_languages = gr.CheckboxGroup(
+                        choices=ALL_LANGUAGE_NAMES,
+                        label="Target languages",
+                        value=["Yoruba"],
+                    )
+                    with gr.Row():
+                        dub_voice = gr.Dropdown(
+                            choices=QWEN_VOICES,
+                            value="Ethan",
+                            label="Voice (for Qwen languages)",
+                            info="Applies to Arabic, French, Spanish, etc. Local languages use default voice.",
+                        )
+                        dub_chunk_slider = gr.Slider(
+                            minimum=30, maximum=300, value=120, step=10,
+                            label="Chunk duration (seconds)",
+                            info="Shorter = more API calls but less timeout risk.",
+                        )
+                    dub_btn = gr.Button("Dub Video", variant="primary", size="lg")
+
+                with gr.Column():
+                    dub_video_out = gr.Video(label="Dubbed video (download from player)")
+                    dub_log = gr.Markdown(
+                        label="Processing log",
+                        value="Upload a video and select languages to start."
+                    )
+
+            dub_btn.click(
+                fn=dub_video,
+                inputs=[dub_video_in, dub_languages, dub_voice, dub_chunk_slider],
+                outputs=[dub_video_out, dub_log],
+            )
+
+        # ====== TAB 4: TEXT TRANSLATION ======
+        with gr.TabItem("Text \u2192 Audio"):
+            gr.Markdown("### Text to translated speech")
+            gr.Markdown("Type English text, choose a language, and hear the translated audio.")
+
+            with gr.Row():
+                text_language = gr.Dropdown(
+                    choices=ALL_LANGUAGE_NAMES,
+                    value="Yoruba",
+                    label="Target language",
+                )
+                text_voice = gr.Dropdown(
+                    choices=get_voices_for_language("Yoruba"),
+                    value=get_voices_for_language("Yoruba")[0],
+                    label="Voice",
+                )
+
+            text_language.change(
+                fn=update_voices,
+                inputs=[text_language],
+                outputs=[text_voice],
+            )
+
+            with gr.Row():
+                with gr.Column():
+                    text_input = gr.Textbox(
+                        label="English text",
+                        placeholder="Type English football commentary here...",
+                        lines=4,
+                    )
+                    text_btn = gr.Button("Translate to speech", variant="primary", size="lg")
+                    gr.Examples(
+                        examples=[[e] for e in EXAMPLES],
+                        inputs=[text_input],
+                        label="Example commentary",
+                    )
+
+                with gr.Column():
+                    text_audio_out = gr.Audio(label="Translated audio", type="numpy", autoplay=True)
+                    text_log = gr.Markdown(label="Pipeline log")
+
+            text_btn.click(
+                fn=full_pipeline_text,
+                inputs=[text_input, text_language, text_voice],
+                outputs=[text_audio_out, text_log],
+            )
+
+        # ====== TAB 5: RECORDINGS ======
+        with gr.TabItem("Recordings & Clips"):
+            gr.Markdown("### Recordings management")
+            gr.Markdown(
+                "Past dubbed recordings will appear here. "
+                "This feature is coming soon — for now, use Video Dubbing to create new recordings "
+                "and download them from the player."
+            )
+
+        # ====== TAB 6: VOICE MODELS ======
+        with gr.TabItem("Voice Models"):
+            gr.Markdown("### Voice model library")
+            gr.Markdown("Browse available voices for each language.")
+
+            voice_lang_select = gr.Dropdown(
+                choices=ALL_LANGUAGE_NAMES,
+                value="Yoruba",
+                label="Select language",
+            )
+            voice_info = gr.Markdown()
+
+            def show_voice_info(lang):
+                config = LANGUAGES.get(lang, {})
+                engine = config.get("tts_engine", "unknown")
+                voices = config.get("yourvoic_voices", [])
+
+                info = f"### {lang}\n\n"
+                if engine == "qwen":
+                    info += f"**Engine:** Qwen 3.5 Omni (end-to-end speech-to-speech)\n\n"
+                    info += f"This is the highest quality option. Qwen handles ASR + translation + TTS in a single API call, "
+                    info += f"preserving tone, emotion, and pacing from the original speaker.\n\n"
+                    info += f"**Available voices ({len(QWEN_VOICES)}):** {', '.join(QWEN_VOICES[:10])}... and {len(QWEN_VOICES)-10} more\n\n"
+                    info += f"All voices support all Qwen languages."
+                elif engine == "yourvoic":
+                    info += f"**Engine:** YourVoic API (TTS) + NLLB-200 (translation)\n\n"
+                    info += f"**YourVoic language:** `{config.get('yourvoic_lang', 'N/A')}`\n\n"
+                    info += f"**Available voices:** {', '.join(voices) if voices else 'Peter (default)'}"
+                else:
+                    info += f"**Engine:** Local pipeline (Whisper ASR + NLLB MT + MMS-TTS)\n\n"
+                    info += f"**NLLB code:** `{config.get('nllb', 'N/A')}`\n\n"
+                    info += "Uses locally fine-tuned models on GPU. Voice selection not available."
+
+                return info
+
+            voice_lang_select.change(fn=show_voice_info, inputs=[voice_lang_select], outputs=[voice_info])
+            demo.load(fn=show_voice_info, inputs=[voice_lang_select], outputs=[voice_info])
+
+    gr.Markdown("""
+---
+**PlotWeaver** by PlotweaverAI | Models:
+[ASR](https://huggingface.co/PlotweaverAI/whisper-small-de-en) |
+[MT](https://huggingface.co/PlotweaverAI/nllb-200-distilled-600M-african-6lang) |
+[TTS](https://huggingface.co/PlotweaverAI/yoruba-mms-tts-new) |
+[YourVoic API](https://yourvoic.com)
+""")
+
+
+if __name__ == "__main__":
+    demo.launch()

packages.txt

@@ -0,0 +1 @@
+ffmpeg

requirements.txt

@@ -0,0 +1,10 @@
+torch>=2.0.0
+torchaudio>=2.0.0
+transformers>=4.36.0
+accelerate>=0.25.0
+soundfile>=0.12.0
+numpy>=1.24.0
+gradio>=5.0.0
+audioop-lts
+requests>=2.28.0
+openai>=1.0.0