Whisper stands for “Web-scale Supervised Pretraining for Speech Recognition” (I know, it should’ve been “WSPSR”). Whisper is a speech model trained in a supervised setup on 680,000 hours of labeled audio data to handle different speech-related tasks such as “transcription”, “translation”, “VAD”, and “alignment” on approximately $100$ languages. Whisper was proposed by OpenAI in 2022 and published in this paper “Robust Speech Recognition via Large-Scale Weak Supervision”. The official code for Whisper can be found on OpenAI’s official GitHub repository: openai/whisper. The following figure shows the architecture of Whisper:


Following the trend of leveraging web-scale text from the internet for training machine learning systems, whisper was trained to utilize the raw text of transcripts without any text normalization step. However, using raw text comes with its own challenges such as:

  • Many of these audio-related texts were machine-generated originally. To overcome that, they developed many heuristics to remove machine-generated transcripts from the training dataset; such that any text with all lower case or upper case are most likely have been generated by another ASR model. Same happens with text with few to no punctuations.

  • Different textual data on the internet uses different encodings. To overcome this, they performed the following steps to normalize English texts to a standarized form:

  • During early development and evaluation, they observed that Whisper had a tendency to transcribe incorrect guesses for the speakers’ names. This happens because many transcripts in the pre-training dataset include the name of the person who is speaking even though it was never mentioned in the audio. To avoid this, they fine-tuned Whisper models briefly on the subset of transcripts that do not include speaker annotations which removes this behavior.

For language detection, they used an internal audio language detector along with Compact Language Detector 2(CLD2) to ensure a higher quality of detecting the spoken language. If the two didn’t match, they don’t include the (audio, transcript) pair as a speech recognition training example in the dataset. However, if the transcript language is English, then they added these pairs to the dataset as X→en speech translation training examples.

Finally, they broke audio files into 30-second segments paired with the subset of the transcript that occurs within that time segment. All audio was re-sampled to $16,000$ Hz, and an $80$-channel logmagnitude Mel spectrogram representation is computed on $25ms$ windows with a stride of $10ms$. All audio were used for training, including segments where there is no speech (though with sub-sampled probability) and use these segments as training data for voice activity detection. The full data consists of $680,000$ hours including $117,000$ hours of multilingual speech in $96$ languages, and $125,000$ hours of x→en translation and the rest is 438,000 hours of English speech.


As shown in the previous graph, Whisper is an encoder-decoder Transformer where the encoder and decoder have the same width and number of transformer blocks. To make it more efficient, they made the following changes to the original architecture:

  • To handle audio log-mel spectrogram, they added a small network to the encoder; consisting of two convolution layers of kernel width ($kw = 3$) and a stride $s = (1,2)$ respectively, and a GELU activation function.

  • They used pre-activation residual blocks; similar to this paper.

  • They added a layer normalization to the encoder output.

  • The decoder uses learned position embeddings and tied input-output token representations; similar to this paper.

  • To handle multiple speech-related tasks, the decoder uses different prompts as shown in the following graph. The decoder’s prompt always consists of three things: special tokens (yellow box), text tokens (blue box), and timestamp tokens (green box).

So, let’s see the prompt used for different tasks:

  • Speech Recognition (en):
  • Speech Translation (es→en):
  • VAD, Voice Activity Detection:
  • Timestamp-predictions; i.e Speech Alignment or Diarization:

In order to study the scaling properties of Whisper, they trained various sizes according to the following table:

Following the release of the paper, the authors announced a “large-v2” model trained for $2.5x$ more epochs with regularization and no architecture changes. This “large-v2” model surpasses the performance of the “large” model.

Training Details

In the paper, they trained Whisper with data parallelism across accelerators using FP16 with dynamic loss scaling and activation check-pointing. All Whisper models were trained with AdamW and gradient norm clipping with a linear learning rate decay to zero after a warmup over the first $2048$ updates. A batch size of $256$ tokens was used, and the models are trained for $220$ updates (around 2-3 epochs over the dataset). Regarding the text tokenizer, they used the same byte-level BPE text tokenizer used in GPT2 for the English-only models and refit the vocabulary (with keeping the same size) for the multilingual models.The following table summarizes the full training hyper-parameters:

Due to only training for a few epochs, over-fitting is not a large concern here. That’s why they didn’t use any data augmentation or regularization. However, Whisper “large-v2” was trained with regularization methods such as Stochastic Depth, and BPE Dropout and augmentation methods such as SpecAugment.

Experiments & Results

Since Whisper was trained on a the biggest audio dataset out there, they evaluated Whisper models in a zero-shot setting without using any of the training data for each of these datasets. Also, since Whisper supports different tasks, they evaluated it separately on each of them as shown below:

Important Note:
All results reported in the paper were obtained after applying their own text normalizer on all models (the baselines & Whisper).

English Speech Recognition

To evaluate Whisper models on English speech recognition task, they used the following datasets: LibriSpeech (“test-clean” & “test-other”), TED-LIUM 3, Common Voice 5.1, Artie bias corpus, CallHome, Switchboard, WSJ, CORAAL, CHiME-6 (synchronized version of CHiME-5), and the AMI corpus. A detailed comparison between Whisper and Wav2vec 2.0 is shown in the following table:

Although both models perform within $0.1\%$ of each other on LibriSpeech, a zero-shot Whisper model performs much better on other datasets than expected for its LibriSpeech performance and makes $55.2\%$ less errors on average.

If you are curious about how other sizes are performing compared to different ASR baselines and human performance, the following figure summarizes the “effective robustness” in a very smart way; “Effective Robustness” measures the performance difference between an in-distribution dataset (x-axis) and out-of-distribution datasets (y-axis).

As you can see from the figure, the smallest Whisper model is roughly competitive with the best supervised LibriSpeech model when evaluated on other datasets.

Long-form English Transcription

As mentioned earlier, Whisper was trained on 30-second audio chunks and cannot consume longer audio inputs at once. This presents challenges in real-world applications which often require transcribing minutes or hours-long audio. To use Whisper on long-audio utterances, they developed a strategy that relies on accurate prediction of the timestamp tokens to determine the amount to shift the model’s 30-second audio context window. The full details about this strategy will be described shortly. To evaluate Whisper on this task, they used several datasets which can be summarized in the following list:

In the paper, they compared the performance with open-source models as well as 4 commercial ASR services. The results are summarized in the following figure showing the distribution of word error rates from Whisper and the 4 commercial ASR services,

The results show that Whisper performs better than the compared models on most datasets, especially on the Meanwhile dataset which is heavy with uncommon words. The following table shows different Whisper sizes in comparison with prior work on the long-form seven datasets:

As mentioned earlier, Whisper is trained on 30-second audio utterances. To use Whisper to work on long-audio utterances, they developed a strategy by doing the following steps:

  • First, they used beam search with $bs = 5$.

  • Then, they used the model’s log probability as the score function, to control the temperature sampling. They started with temperature and increased the temperature by $0.2$ up to $1.0$ when either the average log probability over the generated tokens is lower than $- 1$ or the generated text has a gzip compression rate higher than $2.4$ (which gives an indication of repetition).

  • Finally, to avoid a failure mode where the model ignores the first few words in the input, they constrained the initial timestamp token to be between $0.0$ and $1.0$ second.

  • Also, they noticed that providing the transcribed text from the preceding window as previous-text conditioning when the applied temperature is below $0.5$ further improves the performance.

  • In case of no-speech, they found out that using the <|nospeech|> token alone is not sufficient. Combining it with the average log-probability threshold of $- 1$ makes the voice activity detection of Whisper more reliable.

The following table shows that adding each of the interventions above incrementally reduces the WER overall, but not evenly across the dataset.

Non-English Speech Recognition

In order to compare Whisper models with prior work such as XLS-R, mSLAM, and Maestro, they reported the performance on Mutlilingual LibriSpeech (MLS) and VoxPopuli in the following table:

From the past table, we can see that Whisper significantly under-performs prior work. The authors in the paper think that this under-performance of Whisper models could be due to zero-shot evaluation of Whisper. However, these two benchmarks are narrow since they only include 15 languages, almost all of which are in the Indo-European language family and many of which are high-resource languages.

Speech Translation

To study the translation capabilities of Whisper, they measured its performance on the x→en subset of CoVOST 2 and they compared it with XLS-R, mSLAM, and Maestro and reported the results in the following table which shows that Whisper achieves a new state of the art of $29.1$ BLEU zero-shot without using any of the training data

For an additional analysis on an even wider set of languages, they re-purposed Fleurs dataset, which is a speech recognition dataset, as a translation dataset. They did that by using the English transcripts as reference translations since the same sentences are transcribed for every language. The following figure shows the correlation between the amount of translation training data per language and the resulting zero-shot BLEU score. From the figure, you can see a clear trend of improvement with increasing training data,

Language Identification

To evaluate language identification (LID) task, they used the Fleurs dataset as well. They compared Whisper with prior supervised work and results are reported in the following table where you can see that Whisper under-performs the supervised state-of-the-art model (mSLAM) by$\ 13.6\%$. However, Whisper is heavily disadvantaged since the Whisper dataset contains no data for 20 of the 102 languages in Fleurs. On the $82$ overlapping languages, Whisper achieves $80.3\%$ accuracy.

Noise Robustness

In the paper, they tested Whisper against noise and $14$ LibriSpeech-trained models by measuring the WER when either white noise or pub noise from the Audio Degradation Toolbox was added to the audio. The following figure shows how the ASR performance degrades as the additive noise becomes more intensive:

From the figure, we can see that there are many models that outperform Whisper, which is unsurprising given those models are trained primarily on LibriSpeech, but all models quickly degrade as the noise becomes more intensive, performing worse than the Whisper model under additive pub noise of SNR $- 10$ dB. This showcases Whisper’s robustness to noise, especially under more natural distribution shifts like the pub noise.

Ablation Study

In the paper, they performed some ablations to measure the effect of different aspects of Whisper models such as “model size”, “dataset scale”, and “multitask and multilingual setup”:

  • Model Size:
    As shown in the following figure, with the exception of English speech recognition, performance continues to increase with model size across multilingual speech recognition, speech translation, and language identification. The diminishing returns for English speech recognition could be due to saturation effects from approaching human-level performance.
  • Dataset Scaling:
    To study this, they trained a series of medium-sized models on sub-sampled versions of the dataset which are $0.5\%$, $1\%$, $2\%$, $4\%$, and $8\%$ of the full dataset size and compared their performance with the same medium-sized model trained on the whole dataset. Early stopping based on the validation loss was used to select model checkpoints for each dataset size. Results are reported in the following table which show that the models’ performance improves with increasing dataset size.
  • Multilingual & Multitask Transfer:
    A potential concern with jointly training a single model on many tasks and languages is the possibility of negative transfer. To investigate that, they compared the performance of models trained on just English speech recognition and multilingual & multitask setup and measured their performance English speech recognition task. To make the comparison fair, they adjusted for the amount of FLOPs spent training on the task of English speech recognition as only $65\%$ of compute is spent on this task in a joint training setup. Results are visualized in the following figure which show that for small models trained with moderate amounts of compute, there is indeed negative transfer between tasks and languages. However, multitask and multilingual models scale better and for our larger models demonstrating positive transfer from other tasks.