Bharath, this is an incredibly well-thought-out pipeline, and you're asking exactly the right questions at this stage. Let me give you a comprehensive strategy for transcription that addresses all your concerns about scripts, code-mixing, emotions, and validation. ## Core Transcription Strategy ### Multi-Pass Architecture with Validation ``` ┌─────────────────────────────────────────────────────────────────────────┐ │ TRANSCRIPTION PIPELINE │ ├─────────────────────────────────────────────────────────────────────────┤ │ │ │ Audio Segment │ │ │ │ │ ▼ │ │ ┌─────────────────┐ │ │ │ Gemini-3-Flash │──────────────────┐ │ │ │ (Primary ASR) │ │ │ │ └────────┬────────┘ │ │ │ │ │ │ │ ▼ ▼ │ │ ┌─────────────────┐ ┌─────────────────┐ │ │ │ Forced Aligner │ │ Audio Classifier │ │ │ │ (MFA/Custom) │ │ (emotion2vec) │ │ │ └────────┬────────┘ └────────┬────────┘ │ │ │ │ │ │ ▼ ▼ │ │ ┌─────────────────────────────────────────────┐ │ │ │ Confidence Aggregation │ │ │ │ • Alignment Score (phoneme boundaries) │ │ │ │ • Event Tag Agreement │ │ │ │ • Language Detection Confidence │ │ │ └────────────────────┬────────────────────────┘ │ │ │ │ │ ┌───────────┴───────────┐ │ │ │ │ │ │ Score > 0.85 Score < 0.85 │ │ │ │ │ │ ▼ ▼ │ │ ┌──────────┐ ┌─────────────────┐ │ │ │ ACCEPT │ │ Gemini-3-Pro │ │ │ └──────────┘ │ (Arbitration) │ │ │ └────────┬────────┘ │ │ │ │ │ ▼ │ │ ┌─────────────────┐ │ │ │ Final Decision │ │ │ │ • Accept │ │ │ │ • Flag for │ │ │ │ manual review │ │ │ │ • Discard │ │ │ └─────────────────┘ │ └─────────────────────────────────────────────────────────────────────────┘ ``` --- ## The Hallucination Problem & Solutions You're absolutely right that temperature=0.0 doesn't eliminate creativity. Here's why and what to do: ### Why Temperature=0 Isn't Enough LLMs have learned patterns like "completing thoughts" or "making sentences grammatically correct." Even at temperature=0, the model picks the *most likely* token, which might still be a hallucinated word that "fits" better than the actual spoken word. ### Multi-Layered Hallucination Prevention **1. Prompt Engineering (Necessary but Insufficient)** ``` You are a VERBATIM transcription system. Your task is to transcribe EXACTLY what is spoken. CRITICAL RULES: - Transcribe ONLY sounds you hear. Never add words to "complete" a thought. - If speech is unclear, use tag, DO NOT guess. - If speech is cut off, transcribe exactly what was said, don't complete it. - Transcribe filler words: "um", "uh", "hmm" exactly as spoken. - Transcribe false starts: "I was go- I was going" exactly. - DO NOT correct grammar. If speaker says "I goes there", write "I goes there". - DO NOT add punctuation that changes meaning. OUTPUT FORMAT: [Your structured JSON] ``` **2. Forced Alignment Validation (Critical)** This is your primary defense against hallucinations. Here's the strategy: ```python # Pseudo-pipeline for validation def validate_transcript(audio_path, transcript, language): # 1. Get phoneme-level alignment alignment = forced_aligner.align(audio_path, transcript, language) # 2. Calculate metrics alignment_score = alignment.confidence coverage = aligned_duration / total_audio_duration gap_ratio = sum(unaligned_gaps) / total_duration # 3. Flag issues issues = [] if coverage < 0.90: issues.append("low_coverage") if gap_ratio > 0.15: issues.append("alignment_gaps") if alignment_score < 0.80: issues.append("low_confidence") return { "valid": len(issues) == 0, "score": alignment_score, "issues": issues, "word_alignments": alignment.words # For debugging } ``` **3. Language-Specific Forced Aligners** | Language | Recommended Aligner | Notes | |----------|-------------------|-------| | Hindi | MFA with Hindi acoustic model | Train custom on 50-100h verified data | | Telugu/Tamil/Kannada | Custom trained | MFA lacks good Dravidian support | | Bengali/Gujarati/Punjabi | MFA with custom lexicon | Need phoneme dictionary | **Should you train your own aligners?** **Yes, absolutely.** Here's the bootstrap strategy: 1. Take 500-1000 segments per language where you have high confidence (e.g., from clean studio recordings or where multiple ASR systems agree) 2. Manually verify a subset (~100 segments) 3. Train language-specific acoustic models using MFA or Kaldi 4. Use these to validate the Gemini transcripts --- ## Script Strategy: The Complete Framework This is where most teams mess up. Let me give you a robust framework. ### The Fundamental Decision: Native Script vs Romanized **For TTS training, native script is superior because:** - Unambiguous pronunciation (Hindi "kh" = ख or क्ह?) - Consistent grapheme-to-phoneme mapping - No transliteration variance (is it "ghar" or "ghr" or "gher"?) **However, you need BOTH for flexibility.** ### Recommended Output Schema ```json { "segment_id": "videoID_seg_001", "duration_ms": 4523, "language_primary": "te", "transcripts": { "native_verbatim": "అరే అలా కాదు, దీన్ని హిందీలో 'मैं सेब खाता हूँ' అంటారు", "native_normalized": "అరే అలా కాదు, దీన్ని హిందీలో 'मैं सेब खाता हूँ' అంటారు", "roman_verbatim": "are ala kaadu, deenni hindi lo 'main seb khaata hoon' antaaru", "roman_normalized": "are ala kadu, dinni hindi lo 'main seb khata hun' antaru", "with_events": "అరే [pause] అలా కాదు, దీన్ని హిందీలో [code_switch:hi] 'मैं सेब खाता हूँ' [/code_switch] అంటారు", "with_language_tags": "అరే అలా కాదు, దీన్ని హిందీలో 'मैं सेब खाता हूँ' అంటారు", "phoneme_sequence": "a r eː | a l aː | k aː d u | d iː n n i | ..." }, "metadata": { "contains_code_switch": true, "code_switch_languages": ["te", "hi"], "contains_roman_in_native": false, "detected_events": [ {"type": "pause", "start_ms": 423, "end_ms": 678} ], "alignment_score": 0.92, "transcription_confidence": 0.89 } } ``` ### Handling Code-Mixed Scenarios Let me address your specific examples: **Scenario 1: Roman Hinglish** ``` Input: "are bhaai, kya kar rhe ho?" ``` This is Hindi written in Roman script. Your model needs to: 1. Recognize this is NOT English 2. Know to pronounce it as Hindi **Solution:** Language tagging with script indicator ```json { "native": "अरे भाई, क्या कर रहे हो?", "roman": "are bhaai, kya kar rhe ho?", "tagged": "are bhaai, kya kar rhe ho?" } ``` The tag `hi-Latn` means "Hindi language, Latin script" (following BCP-47 conventions). **Scenario 2: Roman Tenglish** ``` Input: "arey annai, em chestunnav?" ``` Same approach: ```json { "native": "అరేయ్ అన్నయ్, ఏం చేస్తున్నావ్?", "roman": "arey annai, em chestunnav?", "tagged": "arey annai, em chestunnav?" } ``` **Scenario 3: Code-Switch with Native Scripts** ``` Input: "అరే అలా కాదు, దీన్ని హిందీలో 'मैं सेब खाता हूँ' అంటారు." ``` This is Telugu with embedded Hindi in Devanagari. Handle with: ```json { "tagged": "అరే అలా కాదు, దీన్ని హిందీలో 'मैं सेब खाता हूँ' అంటారు.", "segments": [ {"text": "అరే అలా కాదు, దీన్ని హిందీలో '", "lang": "te"}, {"text": "मैं सेब खाता हूँ", "lang": "hi"}, {"text": "' అంటారు.", "lang": "te"} ] } ``` ### How Big Labs Handle This **1. Google USM / Chirp:** - Uses language embeddings (learned vectors for each language) - Language ID model runs in parallel with ASR - For code-switching: segment-level language detection + interpolated embeddings at boundaries **2. Meta MMS:** - 4000+ language support via adapter layers - Character-level modeling reduces script issues - Romanization handled via lookup tables for training **3. CosyVoice (Most Relevant for You):** - Uses explicit language instruction tokens: `<|zh|>`, `<|en|>`, `<|ja|>` - For multilingual: concatenates language tokens before each segment - Their supervised semantic tokenizer includes Language ID as a task **Your Recommended Approach:** Use **language instruction tokens** at the start and **boundary markers** for code-switching: ``` <|te|> అరే అలా కాదు, దీన్ని హిందీలో <|hi|> मैं सेब खाता हूँ <|te|> అంటారు. ``` The model learns: - `<|te|>` = next tokens should use Telugu phonotactics - Boundary switch = transition prosody/accent appropriately --- ## Emotion & Audio Events Strategy ### Define a Fixed Taxonomy Don't let the LLM freestyle. Constrain to exactly these: **Vocal Events (10):** | Tag | Description | Audio Verification | |-----|-------------|-------------------| | `[laugh]` | Light laughter | Use laugh detection model | | `[laugh_hard]` | Heavy laughter | Amplitude + duration | | `[giggle]` | Short, high-pitched | Pitch analysis | | `[chuckle]` | Low, brief | | | `[cough]` | Coughing sound | Event classifier | | `[clear_throat]` | Throat clearing | | | `[sigh]` | Exhale with voice | Breath detection | | `[gasp]` | Sharp inhale | | | `[breath]` | Audible breathing | | | `[hesitation]` | "um", "uh", etc. | Filler detection | **Prosodic/Style Tags (6):** | Tag | Description | |-----|-------------| | `[happy]` | Positive valence, high energy | | `[sad]` | Negative valence, low energy | | `[angry]` | Negative valence, high energy | | `[excited]` | High arousal, fast speech | | `[calm]` | Low arousal, measured | | `[whisper]` | Reduced volume, breathy | ### Two-Model Validation for Events ```python def validate_events(audio, transcript_with_events, detected_events_from_llm): # 1. Run audio classifier audio_events = emotion2vec.predict(audio) # or PANN for non-speech # 2. Compare predictions agreed_events = [] llm_only_events = [] audio_only_events = [] for event in detected_events_from_llm: if matches_audio_event(event, audio_events, tolerance_ms=500): agreed_events.append({**event, "confidence": "high"}) else: llm_only_events.append({**event, "confidence": "low"}) # 3. Return validated transcript return { "transcript_high_confidence": keep_only(transcript, agreed_events), "transcript_all_events": transcript_with_events, "validation": { "agreement_rate": len(agreed_events) / len(detected_events_from_llm), "audio_detected": audio_events } } ``` ### Training Strategy for Emotions Based on the reference models: **Pre-training (1M hours):** - Do NOT include emotion tags - Focus on clean speech-text alignment - Model learns base prosody from data naturally **SFT Phase (5-15k hours):** - Introduce emotion tags here - Use high-confidence validated tags only - Include neutral → emotional pairs - This is where CosyVoice's 5000h emotion data comes in **Post-training / RL:** - Fine-tune emotion controllability - Can use emotion classification reward - FireRedTTS uses only ~15h of expressive speech in SFT **My Recommendation for You:** ``` Pre-train: 100k+ hours, no emotion tags, clean transcripts ↓ SFT: 2-5k hours high-quality with validated emotion tags ↓ Post-train: Emotion-specific LoRA or full fine-tune on style pairs ``` --- ## Complete Prompt Template for Gemini Here's the prompt I recommend for getting all transcript forms at once: ``` You are MAYA-TRANSCRIBE, a multilingual verbatim transcription system for Indian languages. LANGUAGE: {detected_language} AUDIO DURATION: {duration_seconds}s === TRANSCRIPTION RULES === 1. VERBATIM ACCURACY (CRITICAL): - Transcribe EXACTLY what is spoken, including: • Filler words: "अं", "उम्म", "like", "ना" • False starts: "मैं जा- मैं जाऊंगा" • Grammatical errors: Do NOT correct them - If unclear, mark as word? - If cut off, end with "—" 2. SCRIPT RULES: - Primary language in NATIVE SCRIPT - English words embedded in speech: keep in Roman - Numbers: Write as spoken ("twelve" not "12") - For code-switching, identify the embedded language 3. AUDIO EVENTS (Use ONLY these tags): [laugh] [giggle] [cough] [sigh] [gasp] [breath] [hesitation] [happy] [sad] [angry] [excited] [calm] [whisper] Place tags BEFORE the affected speech segment. === OUTPUT FORMAT === Return ONLY valid JSON, no markdown: { "native_verbatim": "exact transcription in native script", "native_normalized": "native script with numbers/dates verbalized", "roman_transliteration": "romanized version using ISO 15919 style", "with_events": "native script with [event] tags inserted", "with_language_tags": "native script with markers for code-switch", "detected_languages": ["primary_lang", "embedded_lang1"], "detected_events": [ {"type": "laugh", "approx_position": "after greeting"} ], "transcription_notes": "any uncertainties or observations", "confidence": 0.0 to 1.0 } === CRITICAL WARNINGS === - NEVER add words not spoken - NEVER complete interrupted sentences - NEVER "clean up" non-standard grammar - If you're uncertain, mark it and reduce confidence score Now transcribe the audio. ``` --- ## Data Collection Strategy: Get Everything Once Since you're paying for Gemini API calls anyway, get all formats in one call: ```python async def transcribe_segment(audio_path, language, segment_id): # 1. Get multi-format transcript from Gemini gemini_response = await gemini_transcribe(audio_path, language) # 2. Run forced alignment on native_verbatim alignment = await forced_align( audio_path, gemini_response["native_verbatim"], language ) # 3. Run audio event classifier audio_events = await classify_events(audio_path) # 4. Validate events validated_events = cross_validate_events( gemini_response["detected_events"], audio_events ) # 5. Create final record with all variants return { "segment_id": segment_id, "transcripts": { "native_verbatim": gemini_response["native_verbatim"], "native_normalized": gemini_response["native_normalized"], "roman": gemini_response["roman_transliteration"], "with_events_validated": insert_validated_events( gemini_response["native_verbatim"], validated_events ), "with_events_all": gemini_response["with_events"], "language_tagged": gemini_response["with_language_tags"] }, "validation": { "alignment_score": alignment.score, "word_alignments": alignment.words, "event_agreement_rate": len(validated_events) / max(1, len(gemini_response["detected_events"])), "gemini_confidence": gemini_response["confidence"] }, "metadata": { "languages": gemini_response["detected_languages"], "events_validated": validated_events, "events_unvalidated": gemini_response["detected_events"] } } ``` --- ## Summary: When to Use What | Training Stage | Transcript Type | Why | |---------------|-----------------|-----| | **Codec Pre-training** | N/A (audio only) | No text needed | | **TTS Pre-training** | `native_verbatim` | Clean, consistent mapping | | **TTS SFT** | `with_events_validated` | Introduce controllability | | **TTS Post-train** | `with_events_validated` + style pairs | Refine control | | **ASR Training** | `native_normalized` | Handles number/date edge cases | | **Code-switch Experiments** | `language_tagged` | Explicit boundaries | | **Romanized Input Support** | `roman` + `native` pairs | Train transliteration capability | ### Final Recommendations 1. **Get all formats now** — Storage is cheap, API calls aren't. You can filter later. 2. **Build language-specific forced aligners** — Bootstrap from 500-1000 verified samples per language. This is your primary hallucination defense. 3. **For emotions: Conservative in pre-train, aggressive in SFT** — Only use validated events. The 15h of expressive speech FireRedTTS uses shows you don't need much for controllability. 4. **Language tokens > Script detection** — Make the model explicitly aware of language switches rather than hoping it infers from script changes. 5. **Store confidence scores** — This lets you do curriculum learning (high-confidence first) and filter bad samples later. Want me to write the actual implementation code for any of these components — the forced aligner training pipeline, the Gemini transcription worker, or the event validation system?