OpenAI Whisper’s amazing power to do fine-tuning demonstrated on Malayalam

Summit 2023 @ Indian Institute of Information Technology, Kottayam (IIIT-K)

Saturday, June 10, 2023

OpenAI Whisper

• I think Whisper¹ is the most under-rated model released by OpenAI.
• It was open-sourced on September 21, 2022 by releasing the inference code and pre-trained model weights.

1. According to research paper p.2, the name Whisper is an abbrevation for WSPR: Web-scale Supervised Pretraining for Speech Recognition.

About OpenAI Whisper Model

• Whisper is a computer program which can listen to people talking and write down what they say. (Automatic Speech Recognition Model)
• Whisper can understand people speaking different languages and can even translate what they say into English. (Supports transcription and translation to English)

Note: This was generated with GPT-4 with prompt: explain what is openai whisper to a 5 year old kid?

Malayalam is a complex language

Comparison of Malayalam TTR with that of European Union Constitution Corpus and DoE-CIIL Corpus from K. Manohar et al.

Picture from Quantitative Analysis of the Morphological Complexity of Malayalam Language by K. Manohar et al. For more information check this paper.

Whisper Event

• HuggingFace Team conducted a whisper fine tuning event for 2 weeks from 5th December 2022 to 19th December 2022. The results were out on 23rd December 2022.
• The goal was to to fine-tune the Whisper model to build state-of-the-art speech recognition systems in the languages of our choice 🗣

Whisper Event huggingface page

Malayalam models produced in Whisper Event

• For the language Malayalam, the results are as follows:

Malayalam models performance in whisper event according to leaderboard

Winning models in Malayalam in Whisper Event

• The winning model for Common voice: thennal/whisper-medium-ml
• The winning model for Fleurs: parambharath/whisper-small-ml

I was not convinced

• Didn’t trust the Hugging Face way of evaluating models.

thennal/whisper-medium-ml model card readme

I was not convinced

• Didn’t trust the Hugging Face way of evaluating models.

Last commit in thennal/whisper-medium-ml

Objective of my benchmarking

• To test whether 10% WER was possible in available academic datasets.

Datasets

• Common Voice 11 malayalam subset
• SMC Malayalam Speech Corpus

Metrics for evaluating ASR models

• ASR evaulation relies on comparission between ground-truth and ASR output.
• Common metrics for ASR evaluation which are popular and good enough¹ are :

1. Word Error Rate(WER)

2. Character Error Rate(CER)

To learn more about ASR evaluation check this blogpost by AWS

1. According to Rethinking Evaluation in ASR: Are our models robust enough? by Likhomanenko T, Xu, Q., Pratap etc.

I wanted to build something new

• New github project for Malayalam ASR Benchmarking

Time for a new adventure

Methadology for benchmarking

1. Create as a python library so further whisper-based transformer models can be benchmark.
2. Calculate WER, CER, model size and time taken to benchmark the model for the listed datasets.
3. Build a reproducible approach, so results of benchmarking is stored as dataset.

Benchmarked models

• Started with 6 fine-tuned models in Malayalam and compared it with 6 model versions released by OpenAI.

1. thennal/whisper-medium-ml
2. parambharat/whisper-tiny-ml
3. parambharat/whisper-base-ml
4. parambharat/whisper-small-ml
5. anuragshas/whisper-large-v2-ml
6. DrishtiSharma/whisper-large-v2-malayalam

Results on benechmarking in Common Voice dataset

Output from benchmarking tool

WER in Common Voice dataset

Word Error Rate in Common Voice-9 test split

CER in Common Voice dataset

Character Error Rate in Common Voice-9 test split

Results on benechmarking in Malayalam Speech Corpus dataset

Output from benchmarking tool

WER in Malayalam Speech Corpus

Word Error Rate in MSC

CER in Malayalam Speech Corpus

Character Error rate in MSC

Links to Project

Github project

https://github.com/kurianbenoy/malayalam_asr_benchmarking

Links to Project

Benchmarking results

• Results on SMC Malayalam Speech corpus

https://huggingface.co/datasets/kurianbenoy/malayalam_msc_benchmarking/tree/main

• Results on Common Voice 11

https://huggingface.co/datasets/kurianbenoy/malayalam_common_voice_benchmarking

Future Ideas for Benchmarking

• Something very similar to OpenLLM Leaderboard with results of latest malayalam speech models.
• Should include results for Kaldi, Meta’s MMS, Wav2Vec etc.

Open LLM leaderboard in huggingface spaces

Conclusion

• In Malayalam we have achieved phenomenal results for fine tuned whisper models.
• The best model is: thennal/whisper-medium-ml
• I think their seems to be a good ASR model suitable for production use-cases.
• You can also do it in your own language especially if it is a low resource language.

Thanks to

1. OpenAI team - Alec Radford, Jong Wook Kim, Christine McLeavey etc. other authors of Whisper paper
2. HuggingFace team - Sanchit Gandhi, Nicolas Patry, Vaibhav Srivastav etc.
3. Kavya Manohar
4. Santhosh Thottingal

5. Thennal D K
6. Other members in Swathanthra Malayalam Computing community.
7. Jarvis Labs