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Whisper Large - AI Audio Models Tool

Overview

Whisper Large is OpenAI's high-capacity automatic speech recognition (ASR) model hosted on Hugging Face. It is an end-to-end Transformer-based ASR system designed for robust, multilingual transcription, speech translation (into English), and language identification. The model is widely used for tasks such as transcribing interviews across many languages, translating podcasts into English, and routing audio to language-specific NLP pipelines. Because it is an open-source, pre-trained model, Whisper Large is commonly adopted in research and production where high transcription accuracy is required and sufficient compute (GPU or optimized CPU runtimes) is available. According to the model page on Hugging Face and the original OpenAI repository, Whisper Large was trained on multilingual, multitask supervised data and is known for wide language coverage and resilience to varied recording conditions (background noise, accents) compared to many smaller models.

Model Statistics

Downloads: 41,518
Likes: 528
Pipeline: automatic-speech-recognition
Parameters: 1.5B

License: apache-2.0

Model Details

Architecture and scale: Whisper Large is an encoder–decoder Transformer model with approximately 1.5 billion parameters (the Hugging Face model card lists 1.5B parameters). It follows the same multimodal training approach described by OpenAI: the model was trained on a very large, diverse multilingual dataset in a multitask setting (automatic speech recognition, language identification, and speech-to-English translation). Training data and inputs: OpenAI reports training on roughly 680,000 hours of supervised audio across many languages, using log-Mel spectrogram inputs sampled at 16 kHz and trained to predict text tokens. The multitask objective enables both direct transcription (original language) and translation-to-English options. Capabilities and usage: Whisper Large supports transcription in ~99 languages, speech-to-English translation, and automatic language identification. In Hugging Face tooling it maps to the automatic-speech-recognition pipeline. Because of its size, users typically run it on GPU for real-time or near-real-time workloads, or use quantization/optimized runtimes for CPU inference. License and hosting: The Hugging Face model for "openai/whisper-large" is published under an open-source license (apache-2.0 according to the model card). Sources: Hugging Face model page and the OpenAI Whisper repository.

Key Features

Multilingual transcription across roughly 99 languages.
End-to-end speech-to-English translation (single-step translation).
Automatic language identification for incoming audio routing.
Robustness to background noise and varied accents from large-scale training.
Open-source model card with apache-2.0 license on Hugging Face.

Example Usage

Example (python):

import whisper

# Load the large Whisper model (requires sufficient RAM/GPU)
model = whisper.load_model("large")

# Transcribe a file in its original language
result = model.transcribe("meeting_audio.mp3", task="transcribe")
print("Transcription:\n", result["text"]) 

# Translate an audio file into English
translation = model.transcribe("foreign_podcast.mp3", task="translate")
print("English translation:\n", translation["text"])