### Key Technical Specifications

- **Model Architecture**: Built on OpenAI's transformer-based encoder-decoder architecture with 1550M parameters. The model uses a sophisticated attention mechanism optimized for speech recognition tasks, with specialized training on diverse multilingual audio data. The architecture includes advanced noise robustness and can handle various audio qualities and recording conditions.

- **Performance Metrics**: 
  Whisper Large v3 sets the benchmark for speech recognition accuracy:
  - Short-form transcription: 8.4% WER (industry-leading accuracy)
  - Sequential long-form: 10.0% WER
  - Chunked long-form: 11.0% WER
  - Multilingual support: 99+ languages
  - Model size: 1550M parameters

### Key Model Details

- **Model Size**: 1550M parameters
- **Speed**: 189x speed factor
- **Audio Context**: Optimized for 30-second audio segments, with a minimum of 10 seconds per segment
- **Supported Audio**: FLAC, MP3, M4A, MPEG, MPGA, OGG, WAV, or WEBM
- **Language**: 99+ languages supported
- **Usage**: [Groq Speech to Text Documentation](/docs/speech-to-text)


### Key Use Cases

#### High-Accuracy Transcription
Perfect for applications where transcription accuracy is paramount:
- Legal and medical transcription requiring precision
- Academic research and interview transcription
- Professional content creation and journalism

#### Multilingual Applications
Ideal for global applications requiring broad language support:
- International conference and meeting transcription
- Multilingual content processing and analysis
- Global customer support and communication tools

#### Challenging Audio Conditions
Excellent for difficult audio scenarios:
- Noisy environments and poor audio quality
- Multiple speakers and overlapping speech
- Technical terminology and specialized vocabulary


### Best Practices

- Prioritize accuracy: Use this model when transcription precision is more important than speed
- Leverage multilingual capabilities: Take advantage of the model's extensive language support for global applications
- Handle challenging audio: Rely on this model for difficult audio conditions where other models might struggle
- Consider context length: For long-form audio, the model works optimally with 30-second segments
- Use appropriate algorithms: Choose sequential long-form for maximum accuracy, chunked for better speed

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## Key Technical Specifications