Whisper Small
Whisper Small is an AI model that can recognize and translate speech with impressive accuracy. Trained on 680,000 hours of audio data, it can handle a wide range of languages and accents, and even translate speech in real-time. But what makes Whisper Small truly remarkable is its ability to generalize to new datasets and domains without needing fine-tuning. This means it can be used in a variety of applications, from speech recognition to speech translation, and even in high-risk domains like decision-making contexts. So, how does it work? Whisper Small uses a Transformer-based encoder-decoder model to analyze audio inputs and generate text outputs. It's also designed to work with audio samples of up to 30 seconds in duration, making it a practical choice for real-world use. And, with its efficient design, it can provide fast and accurate results while keeping costs down. But, it's not perfect, and its performance can vary depending on the language and task. So, it's essential to evaluate its performance in a particular context and domain before deploying it. Overall, Whisper Small is a powerful tool that can revolutionize the way we interact with speech, and its potential applications are vast.
Table of Contents
Model Overview
The Whisper model is a pre-trained AI model for automatic speech recognition (ASR) and speech translation. It was trained on 680,000 hours of labeled data and can generalize to many datasets and domains without fine-tuning.
Capabilities
- Transformer-based encoder-decoder model: Whisper uses a sequence-to-sequence model to predict transcriptions from audio inputs.
- Multilingual support: Whisper can transcribe and translate speech in multiple languages, including English, French, and many others.
- Five model sizes: Whisper comes in five configurations, ranging from “tiny” to “large”, each with varying numbers of parameters.
- Pre-trained checkpoints: All ten pre-trained checkpoints are available on the Hugging Face Hub.
Primary Tasks
- Speech Recognition: Whisper can transcribe audio samples in the same language as the audio.
- Speech Translation: Whisper can translate audio samples from one language to another.
Strengths
- Robustness: Whisper is robust to accents, background noise, and technical language.
- Zero-Shot Translation: Whisper can translate from multiple languages into English without needing to be fine-tuned.
- Improved Performance: Whisper outperforms many existing ASR systems.
Unique Features
- Multi-Language Support: Whisper supports transcription and translation in multiple languages.
- Chunking Algorithm: Whisper can transcribe audio samples of up to arbitrary length using a chunking algorithm.
- Timestamp Prediction: Whisper can predict sequence-level timestamps for transcriptions.
Model Configurations
Whisper comes in five different model sizes, ranging from tiny to large-v2. The smallest four models are trained on either English-only or multilingual data, while the largest models are multilingual only.
| Size | Parameters | English-only | Multilingual |
|---|---|---|---|
| tiny | 39M | ||
| base | 74M | ||
| small | 244M | ||
| medium | 769M | ||
| large | 1550M | x | |
| large-v2 | 1550M | x |
Usage
To use Whisper, you’ll need to pair it with a WhisperProcessor. The processor is used to pre-process audio inputs and post-process model outputs. You can also use the processor to set the context tokens for transcription or translation.
Input Requirements
To use Whisper, you’ll need to provide the following inputs:
- Audio data in the form of log-Mel spectrograms
- Context tokens to specify the task (transcription or translation) and language
Context Tokens
Context tokens are a sequence of tokens that inform the model about the task and language. They consist of:
<|startoftranscript|>token to indicate the start of the transcription- Language token (e.g.,
<|en|>for English) - Task token (
<|transcribe|>for speech recognition or<|translate|>for speech translation) - Optional
<|notimestamps|>token to disable timestamp prediction
You can set the context tokens using the WhisperProcessor:
model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe")
Evaluation and Fine-Tuning
Whisper can be evaluated on various datasets and fine-tuned for specific languages and tasks to improve its performance.
Speed
Whisper is designed to work with audio samples of up to 30 seconds in duration. But what if you need to transcribe longer audio files? No problem! Whisper can be used with a chunking algorithm to transcribe audio samples of arbitrary length.
Accuracy
Whisper’s performance is impressive, with strong results in around 10 languages. It’s particularly good at transcribing English speech, and can even handle accents and background noise with ease.
Efficiency
Whisper is designed to be efficient, with five different model sizes to choose from. The smallest model, “tiny”, has 39 million parameters, while the largest model, “large-v2”, has 1.55 billion parameters.
| Model Size | Parameters |
|---|---|
| tiny | 39M |
| base | 74M |
| small | 244M |
| medium | 769M |
| large | 1.55B |
| large-v2 | 1.55B |
Limitations
While Whisper is a powerful model, it’s not perfect. It may exhibit biases and constraints, particularly in high-risk domains like decision-making contexts. It’s also not intended for use in subjective classification or to infer human attributes.
Limited Context Window
The model is designed to work with audio samples of up to 30 seconds in duration. While it can be used to transcribe longer audio samples using a chunking algorithm, this may not always produce the best results.
Language Limitations
While Whisper has been trained on a large dataset of 680,000 hours of audio, its performance may vary depending on the language being transcribed. The model has been shown to perform well in around 10 languages, but its performance may be lower in languages with less training data.
Background Noise and Accents
While Whisper has been shown to be robust to background noise and accents, it’s not immune to these challenges. In some cases, the model may struggle to accurately transcribe audio with high levels of background noise or strong accents.
Lack of Evaluation in Certain Areas
Whisper has not been robustly evaluated in certain areas, such as voice activity detection, speaker classification, or speaker diarization. While it may exhibit some capabilities in these areas, its performance is not guaranteed.
Potential Biases
As with any AI model, Whisper may exhibit biases in its performance. For example, the model may be more accurate for certain languages or accents than others.
Use in High-Risk Domains
We strongly caution against using Whisper in high-risk domains, such as decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes.
Classification Use Cases
Whisper is not intended for classification use cases, such as inferring human attributes. Its performance in these areas is not evaluated and may not be accurate.
