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Whisper Large v3 - AI Audio Models Tool

Overview

Whisper Large v3 is a state-of-the-art automatic speech recognition (ASR) and speech-to-text translation model released by OpenAI and distributed via Hugging Face. According to the model card, it was trained on more than 5 million hours of supervised audio–text data and is designed for robust zero-shot generalization across languages, accents, and recording conditions. The model is available under the Apache-2.0 license and is offered as a ready-to-run model for the automatic-speech-recognition pipeline on Hugging Face Hub (openai/whisper-large-v3). Whisper Large v3 targets use cases that need high-quality transcription and on-the-fly translation without task-specific fine-tuning. It inherits the encoder–decoder Transformer design from the original Whisper family, providing multilingual recognition (the Whisper family supports recognition for dozens of languages) and the ability to produce translated English outputs. The Hugging Face model page reports wide community adoption—millions of downloads and thousands of likes—making it a common choice for research prototypes and production transcription pipelines where an open-source, high-performance ASR backbone is required (see sources below).

Model Statistics

  • Downloads: 7,127,930
  • Likes: 5288
  • Pipeline: automatic-speech-recognition
  • Parameters: 1.5B

License: apache-2.0

Model Details

Architecture and size: Whisper Large v3 is an encoder–decoder Transformer model in the Whisper family, with approximately 1.5 billion parameters (model card metadata on Hugging Face). It follows the original OpenAI Whisper architecture: audio is converted to log-Mel spectrograms, processed by a Transformer encoder, and decoded token-by-token by a Transformer decoder into text. Capabilities: The model performs multilingual speech-to-text recognition and can also generate English translations from non-English speech (speech-to-translation). It was trained supervised on a very large, diverse audio–text dataset (reported as over 5 million hours), which contributes to strong zero-shot robustness across accents, domains, and noisy recordings. Inputs and outputs: Inputs are audio files or arrays (commonly sampled to 16 kHz and converted to log-Mel features by a processor). Outputs are text transcripts; downstream Hugging Face pipelines return a dict with the predicted text and optional timestamps/segments depending on postprocessing. Deployment notes: The model is large and benefits from GPU inference for latency-sensitive scenarios. It is released under the Apache-2.0 license on Hugging Face and can be used locally or via hosted inference offerings. See the Hugging Face model card and OpenAI Whisper repository for implementation details and preprocessing guidance.

Key Features

  • High-quality multilingual transcription trained on >5M hours of audio.
  • Zero-shot robustness to new accents, speakers, and noisy environments.
  • Speech-to-translation: can output English translations from non-English audio.
  • Encoder–decoder Transformer architecture with 1.5B parameters.
  • Apache-2.0 license for permissive commercial and research use.

Example Usage

Example (python):

from transformers import pipeline

# Simple transcription example using Hugging Face pipeline
asr = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3", device=0)
# Replace 'audio.mp3' with your audio file path (local file or URL supported)
result = asr("audio.mp3")
print(result["text"])

Benchmarks

Parameters: 1.5B (Source: https://huggingface.co/openai/whisper-large-v3)

Training data (reported): Trained on over 5 million hours of audio–text pairs (Source: https://huggingface.co/openai/whisper-large-v3)

Hugging Face downloads: 7,127,930 downloads (Source: https://huggingface.co/openai/whisper-large-v3)

Hugging Face likes: 5,288 likes (Source: https://huggingface.co/openai/whisper-large-v3)

Languages supported (Whisper family): Supports recognition for ~99 languages (Source: https://github.com/openai/whisper)

Last Refreshed: 2026-01-09

Key Information

  • Category: Audio Models
  • Type: AI Audio Models Tool
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