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Federated Finetuning of OpenAI’s Whisper

Federated Finetuning of OpenAI’s Whisper

2023-11-16 13:30:41

Federated Studying has come a good distance because it was formalised by McMahan et al. 2017. Gone are the times the place it was lowered to MNIST-level coaching or equal toy examples with small ML fashions. This blogpost introduces a code instance that takes Open AI鈥檚 Whisper, a state-of-the-art ASR mannequin, and finetunes it for the downstream process of key phrase recognizing. You’ll discover ways to carry out this downstream in a federated method. You could find the whole instance on GitHub.

Federated Studying can leverage giant fashions skilled on publicly out there information and downstream them utilizing wise/non-public information with out having to repeat the info to a central server. Flower takes the coaching to the info supply, a crucial first step in the direction of guaranteeing shopper privateness.

This instance walks you thru the method of designing a Federated Studying pipeline with Flower for key phrase recognizing classification. We鈥檒l use a pre-trained Whisper encoder from 馃 Transformers, freeze its parameters, and federate the training of a classification head to categorise 1-second audio waveforms into one in every of twelve doable courses: ‘sure’, ‘no’, ‘up’, ‘down’, ‘left’, ‘proper’, ‘on’, ‘off’, ‘cease’, ‘go’, a silence, or an unknown phrase. For this instance, we are going to use the Google SpeechCommands dataset.

An summary of the FL pipeline applied with Flower for this instance is proven within the diagram above. It has 4 distinct phases:

  1. In the beginning of a spherical, the server samples some purchasers and sends them the classification head (i.e. the a part of the mannequin being federated).
  2. Every shopper, with a frozen pre-trained Whisper encoder, trains the classification head utilizing its personal information.
  3. As soon as on-site coaching is accomplished, every shopper communicates the up to date classification head again to the server.
  4. The server aggregates the classification heads and obtains a brand new world classification head that can be communicated to purchasers within the subsequent spherical.

The instance out there on GitHub splits the 2112 audio system within the SpeechCommands dataset into 100 teams. Every group may be seen as an workplace with 21 staff. This splitting creates 100 non-iid workplaces, every having completely different quantities of coaching information. We deal with every of those workplaces as a FL shopper. The FL coaching uniformly samples 10 purchasers every spherical and makes use of FedAvg for aggregation. Inside only a few rounds, the key phrase recognizing mannequin can classify unseen key phrases with an accuracy of over 97%. Recall that solely the classification head (which has lower than 0.8 M parameters) is being skilled.

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We used this instance to additionally benchmark the brand new Raspberry Pi 5. It reveals vastly superior efficiency throughout duties in comparison with the earlier Raspberry Pi 4, making it appropriate for demanding on-device coaching workloads just like the one on this instance.

We benchmarked not solely coaching instances but in addition the time taken to pre-process the dataset partitions. A abstract of the outcomes are proven under. With a extra detailed dialogue in code example on GitHub. Occasions are proven in minutes:seconds.

Stage Notes RPi 4 RPi 5
Filter coaching set (~85k rows) doing .filter() in shopper.client_fn 1:58 0:37
Encode 845 rows with WhisperProcessor doing .map() passing utils.prepare_dataset() 1:55 1:06
On-device coaching for 1 epoch (925 examples) finetuning classification head with frozen Whisper encoder 39:45 20:06

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