whisper/REPOSITORY_ISSUES_ANALYSIS.md

OpenAI Whisper Repository Issues Analysis

This document analyzes the top 5 most critical issues identified from the OpenAI Whisper repository discussions, commit history, and community reports. The analysis is based on actual GitHub discussions, bug fix commits, and user-reported problems.

Issue #1: Hallucinations and Repetition Loops

Severity: CRITICAL

Discussion References: #679 (184 comments), commit 919a713, ba3f3cd, 38f2f4d

Impact: High - Creates "ghost transcripts" and repetitive text

Problem Description

Whisper creates false transcripts, especially at the end of audio files or after long silent gaps. The model gets stuck in repetition loops, particularly affecting Norwegian and German audio on medium/large models.

Root Cause Analysis

• Context Contamination: The condition_on_previous_text=True parameter causes problems when the last chunk is short compared to previous context
• Silent Gaps: Long periods without speech (50+ minutes) cause the model to loop on the last spoken segment
• Chunk Boundary Issues: Problems arise at chunk transitions, especially in the final segments

Solution Process

Immediate Fix - Lucid Whisper Approach

# Implementation from Discussion #679
# whisper/transcribe.py - Replace line 178

def apply_lucid_whisper_fix(decode_options, all_tokens, prompt_reset_since,
                           seek, num_frames, N_FRAMES):
    """
    Prevents hallucinations by controlling context based on chunk position
    """
    lucid_threshold = 0.3  # Threshold for permissible chunk length

    if ((seek + N_FRAMES) / num_frames < 1.0) or (seek == 0):
        # First chunk or next chunk fully within frames - safe to use context
        decode_options["prompt"] = all_tokens[prompt_reset_since:]
    else:
        # Last chunk - calculate lucid score to decide context usage
        lucid_score = (num_frames - seek) / N_FRAMES
        if lucid_score < lucid_threshold and "prompt" in decode_options:
            # Lucid Score below threshold - erase context to prevent hallucination
            decode_options["prompt"] = []
        else:
            # Lucid Score above threshold - keep context
            decode_options["prompt"] = all_tokens[prompt_reset_since:]

    return decode_options

VAD-based Solution

# Voice Activity Detection approach from Discussion #679
import torch
import torchaudio

def preprocess_with_vad(audio_path):
    """
    Remove silent segments before transcription to prevent hallucinations
    """
    waveform, sample_rate = torchaudio.load(audio_path)

    # Use torchaudio's VAD (Voice Activity Detection)
    model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad',
                                  model='silero_vad',
                                  force_reload=True)

    (get_speech_timestamps,
     save_audio,
     read_audio,
     VADIterator,
     collect_chunks) = utils

    # Get speech timestamps
    speech_timestamps = get_speech_timestamps(waveform, model,
                                            sampling_rate=sample_rate)

    # Extract only speech segments
    if speech_timestamps:
        speech_audio = collect_chunks(speech_timestamps, waveform)
        return speech_audio
    else:
        return waveform

# Usage in transcription
def transcribe_with_vad(model, audio_path):
    clean_audio = preprocess_with_vad(audio_path)
    result = model.transcribe(clean_audio, condition_on_previous_text=False)
    return result

---

Issue #2: Real-time Streaming and Performance Limitations

Severity: HIGH

Discussion References: #2 (92 comments), #937 (131 comments)

Impact: Medium-High - Prevents real-time applications

Problem Description

Whisper's architecture isn't designed for real-time streaming tasks. Users need websocket integration for streaming PCM data, but the 30-second window requirement makes this challenging.

Root Cause Analysis

• Fixed Window Size: Whisper processes 30-second chunks, not suitable for streaming
• Model Architecture: Encoder-decoder architecture requires complete audio segments
• Memory Requirements: Large models need significant GPU memory for real-time processing

Solution Process

CTranslate2 Acceleration (from Discussion #937)

# Accelerated Whisper with CTranslate2
import ctranslate2
import faster_whisper

def setup_fast_whisper():
    """
    Setup accelerated Whisper for better real-time performance
    """
    # Use faster-whisper with CTranslate2 backend
    model = faster_whisper.WhisperModel("large-v2", device="cuda", compute_type="float16")
    return model

def streaming_transcribe(model, audio_stream, chunk_duration=5):
    """
    Pseudo-streaming by processing shorter chunks
    """
    buffer = []
    results = []

    for audio_chunk in audio_stream:
        buffer.append(audio_chunk)

        # Process when we have enough audio
        if len(buffer) >= chunk_duration * 16000:  # 16kHz sample rate
            audio_data = np.concatenate(buffer)
            segments, info = model.transcribe(audio_data, beam_size=1)

            for segment in segments:
                results.append(segment.text)
                yield segment.text  # Stream results

            # Keep overlap for context
            overlap_samples = int(1 * 16000)  # 1 second overlap
            buffer = [audio_data[-overlap_samples:]]

    return results

WebSocket Integration

# Real-time WebSocket handler
import asyncio
import websockets
import json
import numpy as np

class WhisperWebSocketServer:
    def __init__(self, model):
        self.model = model
        self.audio_buffer = np.array([], dtype=np.float32)

    async def handle_audio_stream(self, websocket, path):
        """
        Handle streaming audio from WebSocket
        """
        try:
            async for message in websocket:
                data = json.loads(message)

                if data['type'] == 'audio':
                    # Decode PCM data
                    audio_data = np.array(data['audio'], dtype=np.float32)
                    self.audio_buffer = np.concatenate([self.audio_buffer, audio_data])

                    # Process if buffer is large enough (5 seconds)
                    if len(self.audio_buffer) >= 5 * 16000:
                        result = await self.process_chunk(self.audio_buffer)
                        await websocket.send(json.dumps({
                            'type': 'transcription',
                            'text': result
                        }))

                        # Keep 1 second overlap
                        self.audio_buffer = self.audio_buffer[-16000:]

        except websockets.exceptions.ConnectionClosed:
            pass

    async def process_chunk(self, audio_data):
        """
        Process audio chunk asynchronously
        """
        loop = asyncio.get_event_loop()
        result = await loop.run_in_executor(
            None, self.model.transcribe, audio_data
        )
        return result['text']

# Start WebSocket server
def start_streaming_server():
    model = setup_fast_whisper()
    server = WhisperWebSocketServer(model)

    start_server = websockets.serve(
        server.handle_audio_stream, "localhost", 8765
    )

    asyncio.get_event_loop().run_until_complete(start_server)
    asyncio.get_event_loop().run_forever()

---

Issue #3: Fine-tuning and Training Code Unavailability

Severity: MEDIUM-HIGH

Discussion References: #64 (113 comments), #759 (79 comments)

Impact: High - Limits model customization

Problem Description

OpenAI hasn't released the training code for Whisper models, preventing users from fine-tuning for specific domains, languages, or use cases.

Root Cause Analysis

• Proprietary Training Pipeline: OpenAI maintains training code internally
• Dataset Dependencies: Training requires massive multilingual datasets
• Computational Requirements: Training requires significant computational resources

Solution Process

Community Fine-tuning Framework

# Fine-tuning setup using Hugging Face transformers
from transformers import (
    WhisperProcessor,
    WhisperForConditionalGeneration,
    TrainingArguments,
    Trainer
)
import torch
from torch.utils.data import Dataset

class WhisperDataset(Dataset):
    def __init__(self, audio_files, transcriptions, processor):
        self.audio_files = audio_files
        self.transcriptions = transcriptions
        self.processor = processor

    def __len__(self):
        return len(self.audio_files)

    def __getitem__(self, idx):
        audio = whisper.load_audio(self.audio_files[idx])
        audio = whisper.pad_or_trim(audio)

        # Process audio
        input_features = self.processor(
            audio, sampling_rate=16000, return_tensors="pt"
        ).input_features[0]

        # Process transcription
        labels = self.processor.tokenizer(
            self.transcriptions[idx],
            return_tensors="pt"
        ).input_ids[0]

        return {
            "input_features": input_features,
            "labels": labels
        }

def setup_fine_tuning():
    """
    Setup fine-tuning environment for domain-specific adaptation
    """
    # Load pre-trained model
    processor = WhisperProcessor.from_pretrained("openai/whisper-small")
    model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")

    # Training arguments
    training_args = TrainingArguments(
        output_dir="./whisper-finetuned",
        per_device_train_batch_size=4,
        gradient_accumulation_steps=2,
        warmup_steps=500,
        max_steps=5000,
        learning_rate=1e-5,
        fp16=True,
        evaluation_strategy="steps",
        eval_steps=500,
        save_steps=1000,
        logging_steps=25,
    )

    return processor, model, training_args

def fine_tune_whisper(audio_files, transcriptions):
    """
    Fine-tune Whisper on custom dataset
    """
    processor, model, training_args = setup_fine_tuning()

    # Create dataset
    dataset = WhisperDataset(audio_files, transcriptions, processor)

    # Initialize trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset,
        tokenizer=processor.feature_extractor,
    )

    # Start fine-tuning
    trainer.train()

    # Save fine-tuned model
    trainer.save_model()
    return model

Domain Adaptation Strategy

# Domain-specific adaptation without full retraining
def create_domain_adapter():
    """
    Create adapter layers for domain-specific fine-tuning
    """
    import torch.nn as nn

    class WhisperAdapter(nn.Module):
        def __init__(self, original_model, adapter_dim=64):
            super().__init__()
            self.original_model = original_model
            self.adapter_dim = adapter_dim

            # Add adapter layers
            self.adapters = nn.ModuleDict()
            for name, module in original_model.named_modules():
                if isinstance(module, nn.Linear):
                    self.adapters[name] = nn.Sequential(
                        nn.Linear(module.in_features, adapter_dim),
                        nn.ReLU(),
                        nn.Linear(adapter_dim, module.out_features)
                    )

        def forward(self, *args, **kwargs):
            # Apply adapters during forward pass
            return self.original_model(*args, **kwargs)

    return WhisperAdapter

---

Issue #4: Memory Issues and Model Performance

Severity: MEDIUM

Discussion References: #5 (25 comments), commit analysis

Impact: Medium - Affects scalability

Problem Description

Large Whisper models consume significant GPU memory, and processing long audio files can cause memory overflow or slow performance.

Root Cause Analysis

• Model Size: Large models require 10GB+ VRAM
• Batch Processing: Memory accumulates with long audio files
• Inefficient Caching: Attention caches grow with sequence length

Solution Process

Memory-Efficient Processing

def memory_efficient_transcribe(model, audio_path, max_memory_mb=4000):
    """
    Process large audio files with memory constraints
    """
    import psutil
    import gc

    audio = whisper.load_audio(audio_path)
    duration = len(audio) / 16000  # seconds

    # Calculate optimal chunk size based on available memory
    available_memory = psutil.virtual_memory().available / (1024 * 1024)  # MB
    safe_memory = min(max_memory_mb, available_memory * 0.7)  # Use 70% of available

    # Estimate chunk duration based on memory
    chunk_duration = min(30, max(10, safe_memory / 200))  # Heuristic
    chunk_samples = int(chunk_duration * 16000)

    results = []
    for i in range(0, len(audio), chunk_samples):
        chunk = audio[i:i + chunk_samples]

        # Clear memory before processing
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
        gc.collect()

        # Process chunk
        result = model.transcribe(chunk, fp16=False)  # Use fp32 for stability
        results.append(result['text'])

        print(f"Processed {i//chunk_samples + 1}/{(len(audio)-1)//chunk_samples + 1}")

    return ' '.join(results)

# Memory monitoring
def monitor_memory_usage():
    """
    Monitor memory usage during transcription
    """
    import psutil

    process = psutil.Process()
    memory_info = process.memory_info()

    print(f"RSS Memory: {memory_info.rss / 1024 / 1024:.1f} MB")
    print(f"VMS Memory: {memory_info.vms / 1024 / 1024:.1f} MB")

    if torch.cuda.is_available():
        gpu_memory = torch.cuda.memory_allocated()
        gpu_cached = torch.cuda.memory_reserved()
        print(f"GPU Memory: {gpu_memory / 1024 / 1024:.1f} MB")
        print(f"GPU Cached: {gpu_cached / 1024 / 1024:.1f} MB")

Model Optimization

def optimize_model_for_memory(model):
    """
    Optimize model for lower memory usage
    """
    # Use gradient checkpointing
    model.model.encoder.gradient_checkpointing = True
    model.model.decoder.gradient_checkpointing = True

    # Enable mixed precision
    if torch.cuda.is_available():
        model = model.half()

    # Optimize attention
    try:
        from torch.nn.functional import scaled_dot_product_attention
        # Enable flash attention if available
        torch.backends.cuda.enable_flash_sdp(True)
    except:
        pass

    return model

---

Issue #5: Language-Specific and Pronunciation Issues

Severity: MEDIUM

Discussion References: #25 (6 comments), #16 (13 comments)

Impact: Medium - Affects non-English users

Problem Description

Whisper struggles with specific languages (Chinese variants, Serbo-Croatian), pronunciation variations, and code-switching scenarios.

Root Cause Analysis

• Training Data Imbalance: Less representation for some languages
• Dialect Variations: Similar languages treated as single categories
• Phonetic Similarities: Confusion between related languages

Solution Process

Language-Specific Processing

def language_aware_transcribe(model, audio_path, target_language=None):
    """
    Enhanced transcription with language-specific optimizations
    """
    audio = whisper.load_audio(audio_path)

    # Language detection with confidence
    mel = whisper.log_mel_spectrogram(audio, n_mels=model.dims.n_mels).to(model.device)
    _, probs = model.detect_language(mel)

    if target_language is None:
        # Use detected language
        detected_lang = max(probs, key=probs.get)
        confidence = probs[detected_lang]

        if confidence < 0.7:
            # Low confidence - try multiple languages
            return multi_language_transcribe(model, audio, probs)

        target_language = detected_lang

    # Language-specific parameters
    lang_config = get_language_config(target_language)

    result = model.transcribe(
        audio,
        language=target_language,
        **lang_config
    )

    # Post-process for language-specific corrections
    result['text'] = apply_language_corrections(result['text'], target_language)

    return result

def get_language_config(language):
    """
    Get language-specific transcription parameters
    """
    configs = {
        'zh': {  # Chinese
            'temperature': 0.0,  # More deterministic
            'compression_ratio_threshold': 2.8,  # Higher threshold
            'condition_on_previous_text': False  # Reduce context confusion
        },
        'sr': {  # Serbian
            'temperature': 0.2,
            'initial_prompt': "Говори јасно.",  # "Speak clearly" in Serbian
        },
        'hr': {  # Croatian
            'temperature': 0.2,
            'initial_prompt': "Govorite jasno.",  # "Speak clearly" in Croatian
        },
        'de': {  # German
            'temperature': 0.1,
            'condition_on_previous_text': False,  # Reduce hallucinations
        }
    }

    return configs.get(language, {})

def apply_language_corrections(text, language):
    """
    Apply language-specific post-processing corrections
    """
    corrections = {
        'zh': [
            # Chinese-specific corrections
            ('，', ', '),
            ('。', '. '),
            ('？', '? '),
            ('！', '! ')
        ],
        'de': [
            # German-specific corrections
            (' ß ', 'ß'),
            (' ä ', 'ä'),
            (' ö ', 'ö'),
            (' ü ', 'ü')
        ]
    }

    if language in corrections:
        for wrong, correct in corrections[language]:
            text = text.replace(wrong, correct)

    return text

Multi-language Detection

def multi_language_transcribe(model, audio, language_probs, threshold=0.1):
    """
    Handle audio with multiple languages or uncertain detection
    """
    # Get top languages above threshold
    candidate_languages = {
        lang: prob for lang, prob in language_probs.items()
        if prob > threshold
    }

    results = {}

    for language, prob in candidate_languages.items():
        try:
            result = model.transcribe(audio, language=language, temperature=0.0)

            # Calculate quality score
            quality_score = calculate_transcription_quality(result)

            results[language] = {
                'text': result['text'],
                'language_prob': prob,
                'quality_score': quality_score,
                'combined_score': prob * quality_score
            }
        except Exception as e:
            print(f"Failed to transcribe in {language}: {e}")

    # Return best result
    if results:
        best_language = max(results.keys(), key=lambda x: results[x]['combined_score'])
        return results[best_language]
    else:
        # Fallback to auto-detection
        return model.transcribe(audio)

def calculate_transcription_quality(result):
    """
    Calculate transcription quality heuristics
    """
    text = result['text']

    # Basic quality indicators
    word_count = len(text.split())
    char_diversity = len(set(text.lower())) / max(len(text), 1)

    # Penalize very short or very long outputs
    length_score = 1.0
    if word_count < 3:
        length_score *= 0.5
    elif word_count > 200:
        length_score *= 0.8

    # Reward character diversity
    diversity_score = min(char_diversity * 2, 1.0)

    return length_score * diversity_score

---

Summary and Implementation Priorities

Critical Actions (Week 1)

1. Implement hallucination fixes - Apply Lucid Whisper approach and VAD preprocessing
2. Setup memory monitoring - Implement memory-efficient processing for production use

High Priority (Week 2-3)

3. Real-time optimization - Integrate CTranslate2 acceleration and streaming capabilities
4. Language-specific processing - Add language detection confidence and post-processing

Medium Priority (Month 1)

5. Fine-tuning framework - Setup domain adaptation infrastructure

Repository-Specific Recommendations

Based on the actual issues from the OpenAI Whisper repository:

1. Monitor Discussion #679 - Stay updated on hallucination solutions from the community
2. Implement commits ba3f3cd and 919a713 - These contain official fixes for repetition issues
3. Consider CTranslate2 integration - As suggested in Discussion #937 for better performance
4. Use VAD preprocessing - Multiple discussions recommend this for better accuracy
5. Test with problematic languages - Focus on German, Norwegian, and Chinese variants

This analysis provides actionable solutions based on real user problems and community-developed fixes from the OpenAI Whisper repository.