--- license: apache-2.0 tags: - whisper-event - generated_from_trainer datasets: - google/fleurs metrics: - wer model-index: - name: Whisper Medium Amharic FLEURS results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: google/fleurs am_et type: google/fleurs config: am_et split: validation args: am_et metrics: - name: Wer type: wer value: 154.41176470588235 --- # Whisper Medium Amharic FLEURS This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the google/fleurs am_et dataset. It achieves the following results on the evaluation set: - Loss: 7.8670 - Wer: 154.4118 ## Model description - The main Whisper Small Hugging Face page: [Hugging Face - Whisper Small](https://huggingface.co/openai/whisper-small) ## Intended uses & limitations - For experimentation and curiosity. - Based on the paper [AXRIV](https://arxiv.org/abs/2212.04356) and [Benchmarking OpenAI Whisper for non-English ASR - Dan Shafer](https://blog.deepgram.com/benchmarking-openai-whisper-for-non-english-asr/), there is a performance bias towards certain languages and curated datasets. - From the Whisper paper, am_et is a low resource language (Table E), with the WER results ranging from 120-229, based on model size. Whisper small WER=120.2, indicating more training time may improve the fine tuning. ## Training and evaluation data - This model was trained/evaluated on "test+validation" data from google/fleurs [google/fluers - HuggingFace Datasets](https://huggingface.co/datasets/google/fleurs). ## Training procedure - The training was done in Lambda Cloud GPU on A100/40GB GPUs, which were provided by OpenAI Community Events [Whisper Fine Tuning Event - Dec 2022](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#fine-tune-whisper). The training was done using [HuggingFace Community Events - Whisper - run_speech_recognition_seq2seq_streaming.py](https://github.com/huggingface/community-events/blob/main/whisper-fine-tuning-event/run_speech_recognition_seq2seq_streaming.py) using the included [whisper_python_am_et.ipynb](https://huggingface.co/drmeeseeks/whisper-small-am_et/blob/main/am_et_fine_tune_whisper_streaming_colab_RUNNING-evalerrir.ipynb) to setup the Lambda Cloud GPU/Colab environment. For Colab, you must reduce the train batch size to the recommended amount mentioned at , as the T4 GPUs have 16GB of memory [Whisper Fine Tuning Event - Dec 2022](https://github.com/huggingface/community-events/tree/main/whisper-fine-tuning-event#fine-tune-whisper). The notebook sets up the environment, logs into your huggingface account, and generates a bash script. The bash script generated in the IPYNB, `run.sh` was run from the terminal to train `bash run.sh`, as described on the Whisper community events GITHUB page. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 3000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:--------:| | 0.0194 | 100.0 | 100 | 3.8540 | 147.9947 | | 0.0001 | 200.0 | 200 | 4.1479 | 148.1283 | | 0.0001 | 300.0 | 300 | 4.1840 | 150.5348 | | 0.0001 | 400.0 | 400 | 4.3339 | 177.9412 | | 0.0 | 500.0 | 500 | 4.5831 | 151.0695 | | 0.0 | 600.0 | 600 | 4.9317 | 164.0374 | | 0.0 | 700.0 | 700 | 5.3031 | 141.0428 | | 0.0 | 800.0 | 800 | 5.6584 | 122.3262 | | 0.0 | 900.0 | 900 | 5.9711 | 157.4866 | | 0.0 | 1000.0 | 1000 | 6.2465 | 141.1765 | | 0.0 | 1100.0 | 1100 | 6.4832 | 169.6524 | | 0.0 | 1200.0 | 1200 | 6.6890 | 155.0802 | | 0.0 | 1300.0 | 1300 | 6.8679 | 159.7594 | | 0.0 | 1400.0 | 1400 | 7.0250 | 155.0802 | | 0.0 | 1500.0 | 1500 | 7.1615 | 146.2567 | | 0.0 | 1600.0 | 1600 | 7.2877 | 143.0481 | | 0.0 | 1700.0 | 1700 | 7.3987 | 148.5294 | | 0.0 | 1800.0 | 1800 | 7.5010 | 142.5134 | | 0.0 | 1900.0 | 1900 | 7.5849 | 136.7647 | | 0.0 | 2000.0 | 2000 | 7.6689 | 148.2620 | | 0.0 | 2100.0 | 2100 | 7.6955 | 165.3743 | | 0.0 | 2200.0 | 2200 | 7.7247 | 162.9679 | | 0.0 | 2300.0 | 2300 | 7.7557 | 161.6310 | | 0.0 | 2400.0 | 2400 | 7.7842 | 162.2995 | | 0.0 | 2500.0 | 2500 | 7.8074 | 150.9358 | | 0.0 | 2600.0 | 2600 | 7.8287 | 154.8128 | | 0.0 | 2700.0 | 2700 | 7.8434 | 155.4813 | | 0.0 | 2800.0 | 2800 | 7.8567 | 154.4118 | | 0.0 | 2900.0 | 2900 | 7.8635 | 154.4118 | | 0.0 | 3000.0 | 3000 | 7.8670 | 154.4118 | ### Recommendations Limit training duration for smaller datasets to ~ 2000 to 3000 steps to avoid overfitting. 5000 steps using the [HuggingFace - Whisper Small](https://huggingface.co/openai/whisper-small) takes ~ 5hrs on A100 GPUs (1hr/1000 steps). Encountered `RuntimeError: The size of tensor a (504) must match the size of tensor b (448) at non-singleton dimension 1` which is related to [Trainer RuntimeError](https://discuss.huggingface.co/t/trainer-runtimeerror-the-size-of-tensor-a-462-must-match-the-size-of-tensor-b-448-at-non-singleton-dimension-1/26010) as some languages datasets have input lengths that have non-standard lengths. The link did not resolve my issue, and appears elsewhere too [Training languagemodel – RuntimeError the expanded size of the tensor (100) must match the existing size (64) at non singleton dimension 1](https://hungsblog.de/en/technology/troubleshooting/training-languagemodel-runtimeerror-the-expanded-size-of-the-tensor-100-must-match-the-existing-size-64-at-non-singleton-dimension-1/). To circumvent this issue, `run.sh` paremeters are adjusted. Then run `python run_eval_whisper_streaming.py --model_id="openai/whisper-small" --dataset="google/fleurs" --config="am_et" --batch_size=32 --max_eval_samples=64 --device=0 --language="am"` to find the WER score manually. Otherwise, erroring out during evaluation prevents the trained model from loading to HugginFace. Based on the paper [AXRIV](https://arxiv.org/abs/2212.04356) and [Benchmarking OpenAI Whisper for non-English ASR - Dan Shafer](https://blog.deepgram.com/benchmarking-openai-whisper-for-non-english-asr/), there is a performance bias towards certain languages and curated datasets. The OpenAI fintuning community event provided ample _free_ GPU time to help develop the model further and improve WER scores. ### Environmental Impact Carbon emissions were estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). In total roughly 100 hours were used primarily in US East/Asia Pacific (80%/20%), with AWS as the reference. Additional resources are available at [Our World in Data - CO2 Emissions](https://ourworldindata.org/co2-emissions) - __Hardware Type__: AMD EPYC 7J13 64-Core Processor (30 core VM) 197GB RAM, with NVIDIA A100-SXM 40GB - __Hours Used__: 100 hrs - __Cloud Provider__: Lambda Cloud GPU - __Compute Region__: US East/Asia Pacific - __Carbon Emitted__: 12 kg (GPU) + 13 kg (CPU) = 25 kg (the weight of 3 gallons of water) ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.13.1+cu117 - Datasets 2.8.1.dev0 - Tokenizers 0.13.2 ### Citation - [Whisper - GITHUB](https://github.com/openai/whisper) - [Whisper - OpenAI - BLOG](https://openai.com/blog/whisper/) - [Model Card - HuggingFace Hub - GITHUB](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md) ```bibtex @misc{https://doi.org/10.48550/arxiv.2212.04356, doi = {10.48550/ARXIV.2212.04356}, url = {https://arxiv.org/abs/2212.04356}, author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya}, keywords = {Audio and Speech Processing (eess.AS), Computation and Language (cs.CL), Machine Learning (cs.LG), Sound (cs.SD), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Robust Speech Recognition via Large-Scale Weak Supervision}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } @article{owidco2andothergreenhousegasemissions, author = {Hannah Ritchie and Max Roser and Pablo Rosado}, title = {CO₂ and Greenhouse Gas Emissions}, journal = {Our World in Data}, year = {2020}, note = {https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions} } ```