---
license: cc-by-nc-4.0
language:
  - en
  - de
  - fr
  - es
  - it
  - pt
  - nl
  - ja
  - zh
  - ru
base_model:
  - openai/whisper-large-v3
library_name: ctranslate2
pipeline_tag: automatic-speech-recognition
tags:
  - whisper
  - speech-recognition
  - german
  - ctranslate2
  - faster-whisper
  - audio
  - transcription
  - multilingual
---

# Whisper Large v3 German - Faster Whisper


## Overview

This repository contains a high-performance German speech recognition model based on OpenAI's **Whisper Large v3** architecture. The model has been optimized using CTranslate2 for faster inference and reduced memory usage, making it ideal for production deployments.

## Model Details

- **Architecture**: Whisper Large v3
- **Language**: Multilingual and German (de) [primary data trained on]
- **Parameters**: ~1.5B
- **Format**: CTranslate2 optimized (`int8_float16`)
- **License**: cc-by-nc-4.0 [![CC BY-NC 4.0](https://licensebuttons.net/l/by-nc/4.0/88x31.png)](https://creativecommons.org/licenses/by-nc/4.0/)

**While this model is optimized for German, it can also transcribe multiple languages supported by Whisper Large v3 (French, Spanish, Italian, etc.), though accuracy may vary depending on the language.**

## Original Model

This model is based on the work from [primeline/whisper-large-v3-german](https://huggingface.co/primeline/whisper-large-v3-german) and has been converted to CTranslate2 format for optimal performance with faster-whisper.

## Performance

The model achieves state-of-the-art performance on German speech recognition tasks with a Word Error Rate (WER) of **2.628%** on comprehensive test datasets (inherited from the base model).

### User Benchmark: NVIDIA GeForce RTX 4070 Laptop GPU

| Metric                     | Value                      |
| -------------------------- | -------------------------- |
| **Audio Duration**         | 198.16 seconds             |
| **Transcription Time**     | 5.83 seconds               |
| **Real-time Factor (RTF)** | 0.0294                     |
| **Speedup**                | ~34x faster than real-time |
| **Compute Type**           | int8_float16               |
| **Batch Size**             | 1                          |

_Note: The model is extremely efficient, processing over 3 minutes of audio in under 6 seconds._

## Use Cases

This model is designed for various German speech recognition applications:

- **Real-time Transcription**: Live audio transcription for meetings, lectures, and conferences
- **Media Processing**: Automatic subtitle generation for German video content
- **Voice Assistants**: Speech-to-text conversion for voice-controlled applications
- **Call Center Analytics**: Transcription and analysis of customer service calls
- **Accessibility Tools**: Converting spoken German to text for hearing-impaired users
- **Document Creation**: Voice-to-text dictation for content creation

## Installation and Usage

### Prerequisites

```bash
pip install faster-whisper
```

### Basic Usage

```python
from faster_whisper import WhisperModel

# Load the model
model = WhisperModel(
    "TheChola/whisper-large-v3-german-faster-whisper",
    device="cuda",
    compute_type="int8_float16"
)

# Transcribe audio file
segments, info = model.transcribe("audio.mp3", language=None, task="transcribe")

print(f"Detected language: {info.language} (probability: {info.language_probability:.2f})")

for segment in segments:
    print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
```

### Advanced Usage (Optimized)

```python
segments, info = model.transcribe(
    "audio.mp3",
    language=None,                  # Auto-detect language
    task="transcribe",
    beam_size=5,
    vad_filter=True,                # Filter silence
    vad_parameters=dict(min_silence_duration_ms=500),
    condition_on_previous_text=False # Prevent loops/skipping
)
```

## Model Files

This repository contains the following files:

- `model.bin` - Main model weights in CTranslate2 format
- `config.json` - Model configuration
- `tokenizer.json` - Tokenizer configuration
- `vocabulary.json` - Vocabulary mapping
- `preprocessor_config.json` - Preprocessing details

## License

This work is licensed under a [Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/).