Whisper JAX

This repository contains optimised JAX code for OpenAI's Whisper Model, largely built on the 🤗 Hugging Face Transformers Whisper implementation. Compared to OpenAI's PyTorch code, Whisper JAX runs over 70x faster, making it the fastest Whisper implementation available.

The JAX code is compatible on CPU, GPU and TPU, and can be run standalone (see Pipeline Usage) or as an inference endpoint (see Creating an Endpoint).

For a quick-start guide to running Whisper JAX on a Cloud TPU, refer to the following Kaggle notebook, where we transcribe 30 mins of audio in approx 30 sec:

The Whisper JAX model is also running as a demo on the Hugging Face Hub:

Installation

Whisper JAX was tested using Python 3.9 and JAX version 0.4.5. Installation assumes that you already have the latest version of the JAX package installed on your device. You can do so using the official JAX installation guide: https://github.com/google/jax#installation

Once the appropriate version of JAX has been installed, Whisper JAX can be installed through pip:

pip install git+https://github.com/sanchit-gandhi/whisper-jax.git

To update the Whisper JAX package to the latest version, simply run:

pip install --upgrade --no-deps --force-reinstall git+https://github.com/sanchit-gandhi/whisper-jax.git

Pipeline Usage

The recommended way of running Whisper JAX is through the FlaxWhisperPipline abstraction class. This class handles all the necessary pre- and post-processing, as well as wrapping the generate method for data parallelism across accelerator devices.

Whisper JAX makes use of JAX's pmap function for data parallelism across GPU/TPU devices. This function is Just In Time (JIT) compiled the first time it is called. Thereafter, the function will be cached, enabling it to be run in super-fast time:

from whisper_jax import FlaxWhisperPipline

# instantiate pipeline
pipeline = FlaxWhisperPipline("openai/whisper-large-v2")

# JIT compile the forward call - slow, but we only do once
text = pipeline("audio.mp3")

# used cached function thereafter - super fast!!
text = pipeline("audio.mp3")

Half-Precision

The model computation can be run in half-precision by passing the dtype argument when instantiating the pipeline. This will speed-up the computation quite considerably by storing intermediate tensors in half-precision. There is no change to the precision of the model weights.

For most GPUs, the dtype should be set to jnp.float16. For A100 GPUs or TPUs, the dtype should be set to jnp.bfloat16:

from whisper_jax import FlaxWhisperPipline
import jax.numpy as jnp

# instantiate pipeline in bfloat16
pipeline = FlaxWhisperPipline("openai/whisper-large-v2", dtype=jnp.bfloat16)

Batching

Whisper JAX also provides the option of batching a single audio input across accelerator devices. The audio is first chunked into 30 second segments, and then chunks dispatched to the model to be transcribed in parallel. The resulting transcriptions are stitched back together at the boundaries to give a single, uniform transcription. In practice, batching provides a 10x speed-up compared to transcribing the audio samples sequentially, with a less than 1% penalty to the WER¹, provided the batch size is selected large enough.

To enable batching, pass the batch_size parameter when you instantiate the pipeline:

from whisper_jax import FlaxWhisperPipline

# instantiate pipeline with batching
pipeline = FlaxWhisperPipline("openai/whisper-large-v2", batch_size=16)

Task

By default, the pipeline transcribes the audio file in the language it was spoken in. For speech translation, set the task argument to "translate":

# translate
text = pipeline("audio.mp3", task="translate")

Timestamps

The FlaxWhisperPipline also supports timestamp prediction. Note that enabling timestamps will require a second JIT compilation of the forward call, this time including the timestamp outputs:

# transcribe and return timestamps
outputs = pipeline("audio.mp3",  task="transcribe", return_timestamps=True)
text = outputs["text"]  # transcription
chunks = outputs["chunks"]  # transcription + timestamps

Putting it all together

In the following code snippet, we instantiate the model in bfloat16 precision with batching enabled, and transcribe the audio file returning timestamps tokens:

from whisper_jax import FlaxWhisperPipline
import jax.numpy as jnp

# instantiate pipeline with bfloat16 and enable batching
pipeline = FlaxWhisperPipline("openai/whisper-large-v2", dtype=jnp.bfloat16, batch_size=16)

# transcribe and return timestamps
outputs = pipeline("audio.mp3",  task="transcribe", return_timestamps=True)

Model Usage

The Whisper JAX model can use on a more granular level in much the same way as the original Hugging Face Transformers implementation. This requires the Whisper processor to be loaded separately to the model to handle the pre- and post-processing, and the generate function to be wrapped using pmap by hand:

import jax.numpy as jnp
from datasets import load_dataset
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from jax import device_get, pmap
from transformers import WhisperProcessor

from whisper_jax import FlaxWhisperForConditionalGeneration

# load the processor and model
processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2")
model, params = FlaxWhisperForConditionalGeneration.from_pretrained(
    "openai/whisper-large-v2", dtype=jnp.bfloat16, _do_init=False,
)

def generate_fn(input_features):
    pred_ids = model.generate(
        input_features, task="transcribe", return_timestamps=False, max_length=model.config.max_length, params=params,
    )
    return pred_ids.sequences

# pmap the generate function for data parallelism
p_generate = pmap(generate_fn, "input_features")
# replicate the parameters across devices
params = replicate(params)

# load a dummy sample from the LibriSpeech dataset
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
sample = ds[0]["audio"]

# pre-process: convert the audio array to log-mel input features
input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="np").input_features
# replicate the input features across devices for DP
input_features = shard(input_features)

# run the forward pass (JIT compiled the first time it is called)
pred_ids = p_generate(input_features)
output_ids = device_get(pred_ids.reshape(-1, model.config.max_length))

# post-process: convert tokens ids to text string
transcription = processor.batch_decode(pred_ids, skip_special_tokens=True)

Available Models and Languages

All Whisper models on the Hugging Face Hub with Flax weights are compatible with Whisper JAX. This includes, but is not limited to, the official OpenAI Whisper checkpoints:

Size	Parameters	English-only	Multilingual
tiny	39 M	✓	✓
base	74 M	✓	✓
small	244 M	✓	✓
medium	769 M	✓	✓
large	1550 M	x	✓
large-v2	1550 M	x	✓

Should you wish to use a fine-tuned Whisper checkpoint in Whisper JAX, you should first convert the PyTorch weights to Flax. This is straightforward through use of the from_pt argument, which will convert the PyTorch state dict to a frozen Flax parameter dictionary on the fly. You can then push the converted Flax weights to the Hub to be used directly in Flax the next time they are required. Note that converting weights from PyTorch to Flax requires both PyTorch and Flax to be installed.

For example, to convert the fine-tuned checkpoint sanchit-gandhi/whisper-small-hi from the blog post Fine-Tuning Whisper:

from whisper_jax import FlaxWhisperForConditionalGeneration, FlaxWhisperPipline
import jax.numpy as jnp

checkpoint_id = "sanchit-gandhi/whisper-small-hi"
# convert PyTorch weights to Flax
model = FlaxWhisperForConditionalGeneration.from_pretrained(checkpoint_id, from_pt=True)
# push converted weights to the Hub
model.push_to_hub(checkpoint_id)

# now we can load the Flax weights directly as required
pipeline = FlaxWhisperPipline(checkpoint_id, dtype=jnp.bfloat16, batch_size=16)

Advanced Usage

More advanced users may wish to explore different parallelisation techniques. The Whisper JAX code is built on-top of the T5x codebase, meaning it can be run using model, activation, and data parallelism using the T5x partitioning convention. To use T5x partitioning, the logical axis rules and number of model partitions must be defined. For more details, the user is referred to the official T5x partitioning guide: https://github.com/google-research/t5x/blob/main/docs/usage/partitioning.md

Pipeline

The following code snippet demonstrates how data parallelism can be achieved using the pipeline shard_params method in an entirely equivalent way to pmap:

from whisper_jax import FlaxWhisperPipline
import jax.numpy as jnp

# 2D parameter and activation partitioning for DP
logical_axis_rules_dp = (
    ("batch", "data"),
    ("mlp", None),
    ("heads", None),
    ("vocab", None),
    ("embed", None),
    ("embed", None),
    ("joined_kv", None),
    ("kv", None),
    ("length", None),
    ("num_mel", None),
    ("channels", None),
)

pipeline = FlaxWhisperPipline("openai/whisper-large-v2", dtype=jnp.bfloat16, batch_size=16)
pipeline.shard_params(num_mp_partitions=1, logical_axis_rules=logical_axis_rules_dp)

Model

It is also possible to use the Whisper JAX model with T5x partitioning by defining a T5x inference state and T5x partitioner:

import jax
import jax.numpy as jnp
from flax.core.frozen_dict import freeze
from jax.sharding import PartitionSpec as P

from whisper_jax import FlaxWhisperForConditionalGeneration, InferenceState, PjitPartitioner


# 2D parameter and activation partitioning for DP
logical_axis_rules_dp = [
    ("batch", "data"),
    ("mlp", None),
    ("heads", None),
    ("vocab", None),
    ("embed", None),
    ("embed", None),
    ("joined_kv", None),
    ("kv", None),
    ("length", None),
    ("num_mel", None),
    ("channels", None),
]

model, params = FlaxWhisperForConditionalGeneration.from_pretrained(
    "openai/whisper-large-v2",
    _do_init=False,
    dtype=jnp.bfloat16,
)


def init_fn():
    input_shape = (1, 80, 3000)

    input_features = jnp.zeros(input_shape, dtype="f4")
    input_features = input_features.at[(..., -1)].set(model.config.eos_token_id)

    decoder_input_ids = jnp.zeros((input_shape[0], 1), dtype="i4")
    decoder_attention_mask = jnp.ones_like(decoder_input_ids)

    batch_size, sequence_length = decoder_input_ids.shape
    decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))

    rng = jax.random.PRNGKey(0)
    init_params = model.module.init(
        rng,
        input_features=input_features,
        decoder_input_ids=decoder_input_ids,
        decoder_attention_mask=decoder_attention_mask,
        decoder_position_ids=decoder_position_ids,
        return_dict=False,
    )
    return init_params


# Axis names metadata
param_axes = jax.eval_shape(init_fn)["params_axes"]

# Create InferenceState, since the partitioner expects it
state = InferenceState(
    step=jnp.array(0),
    params=freeze(model.params_shape_tree),
    params_axes=freeze(param_axes),
    flax_mutables=None,
    flax_mutables_axes=param_axes,
)

# Define the pjit partitioner with 1 model partition
partitioner = PjitPartitioner(
    num_partitions=1,
    logical_axis_rules=logical_axis_rules_dp,
)

mesh_axes = partitioner.get_mesh_axes(state)
params_spec = mesh_axes.params

p_shard_params = partitioner.partition(model.to_bf16, (params_spec,), params_spec)


def generate(params, input_features):
    output_ids = model.generate(input_features, params=params, max_length=model.config.max_length).sequences
    return output_ids


p_generate = partitioner.partition(
    generate,
    in_axis_resources=(params_spec, P("data")),
    out_axis_resources=P("data"),
)

# This will auto-magically run in mesh context
params = p_shard_params(freeze(params))

# you can now run the forward pass with: 
# pred_ids = p_generate(input_features)

Benchmarks

We compare Whisper JAX to the official OpenAI implementation and the 🤗 Transformers implementation. We benchmark the models on audio samples of increasing length and report the average inference time in seconds over 10 repeat runs. For all three systems, we pass a pre-loaded audio file to the model and measure the time for the forward pass. Leaving the task of loading the audio file to the systems adds an equal offset to all the benchmark times, so the actual time for loading and transcribing an audio file will be higher than the reported numbers.

OpenAI and Transformers both run in PyTorch on GPU. Whisper JAX runs in JAX on GPU and TPU. OpenAI transcribes the audio sequentially in the order it is spoken. Both Transformers and Whisper JAX use a batching algorithm, where chunks of audio are batched together and transcribed in parallel (see section Batching).

Table 1: Average inference time in seconds for audio files of increasing length. GPU device is a single A100 40GB GPU. TPU device is a single TPU v4-8.

	OpenAI	Transformers	Whisper JAX	Whisper JAX
Framework	PyTorch	PyTorch	JAX	JAX
Backend	GPU	GPU	GPU	TPU
1 min	13.8	4.54	1.72	0.45
10 min	108.3	20.2	9.38	2.01
1 hour	1001.0	126.1	75.3	13.8

Creating an Endpoint

The Whisper JAX model is running as a demo on the Hugging Face Hub:

However, at peak times there may be a queue of users that limit how quickly your audio input is transcribed. In this case, you may benefit from running the model yourself, such that you have unrestricted access to the Whisper JAX model.

If you are just interested in running the model in a standalone Python script, refer to the Kaggle notebook Whisper JAX TPU:

Otherwise, we provide all the necessary code for creating an inference endpoint. To obtain this code, first clone the repository on the GPU/TPU on which you want to host the endpoint:

git clone https://github.com/sanchit-gandhi/whisper-jax

And then install Whisper JAX from source, with the required additional endpoint dependencies:

cd whisper-jax
pip install -e .["endpoint"]

We recommend that you set-up an endpoint in the same zone/region as the one you are based in. This reduces the communication time between your local machine and the remote one, which can significantly reduce the overall request time.

Gradio App

The Python script app.py contains the code to launch a Gradio app with the Whisper large-v2 model. By default, it uses a batch size of 16 and bfloat16 half-precision. You should update these parameters depending on your GPU/TPU device (as explained in the sections on Half-precision and Batching).

We can launch the Gradio app on port 7860 (default) on our GPU/TPU device through the following command:

python app/app.py

This will launch a Gradio demo with the same interface as the official Whisper JAX demo. To view the Gradio app remotely, we have two options:

1. Open the port 7860 on the GPU/TPU device to listen to all requests
2. Start an ngrok server on the GPU/TPU that redirects requests to port 7860

To open the port 7860 on your GPU/TPU, refer to your hardware provider's firewall instructions (for GCP, these can be found here). Once you have opened port 7860, you should be able to access the gradio demo through the http address:

http://DEVICE-IP:7860

where DEVICE-IP is the public IP address of your GPU/TPU. We can verify this address is accessible by opening this http address in a browser window on our local machine.

Alternatively, we can direct network requests to the Gradio app using ngrok. By using ngrok, we don't need to open the port 7860 on our GPU/TPU - ngrok will provide us with a public http address that will automatically redirect requests to port 7860 on our accelerator. However, in our experience, using ngrok was less reliable than a direct tunnel to port 7860, thus we recommend option 1 here where possible.

To set-up ngrok on your GPU/TPU, first install ngrok according to the official installation guide. You should authenticate your ngrok account if you have one, otherwise your ngrok server will be time-limited to 2 hours. Once installed and authenticated, you can launch an ngrok server on port 7860:

ngrok http 7860

The ngrok http address will be of the form:

https://NGROK-ADDRESS.ngrok.io

which can be used to access the Gradio demo through a web browser.

Sending Requests

Independent of whether you've chosen to open the port 7860 or use ngrok, we're now ready to send audio file requests to our endpoint. To do this, we'll make use of the gradio_client library. If you already have a recent version of Gradio, then the gradio_client library is included as a dependency.

Otherwise, the lightweight gradio_client package can be installed from pip and is tested to work with Python versions 3.9 or higher:

pip install --upgrade gradio_client

We can now send json requests to our endpoint using ngrok. The function transcribe_audio sends an audio file to our endpoint and returns the transcription:

from gradio_client import Client

# make sure this URL matches your http web address
API_URL = "http://DEVICE-IP:7860/" # if using port 7860
API_URL = "https://NGROK-ADDRESS.ngrok.io/" # if using ngrok

# set up the Gradio client
client = Client(API_URL)

def transcribe_audio(audio_path, task="transcribe", return_timestamps=False):
    """Function to transcribe an audio file using our endpoint"""
    text, runtime = client.predict(
        audio_path,
        task,
        return_timestamps,
        api_name="/predict_1",
    )
    return text

# transcribe an audio file using our endpoint
output = transcribe_audio("audio.mp3")

# transcribe with timestamps
output_with_timestamps = transcribe_audio("audio.mp3", return_timestamps=True)

Acknowledgements

• 🤗 Hugging Face Transformers for the base Whisper implementation, particularly to andyehrenberg for the Flax Whisper PR and ArthurZucker for the batching algorithm
• Gradio for their easy-to-use package for building ML demos, and pcuenca for the help in hooking the demo up to the TPU
• Google's TPU Research Cloud (TRC) programme for Cloud TPUs
• Google's t5x Repository for the model partitioning framework

Footnotes

1. See WER results from Colab: https://colab.research.google.com/drive/1rS1L4YSJqKUH_3YxIQHBI982zso23wor?usp=sharing ↩