Update README.md

README.md

@@ -1,35 +1,58 @@
 ---
 license: apache-2.0
 tags:
+- whisper-event
 - generated_from_trainer
 datasets:
-- fleurs
+- google/fleurs
+metrics:
+- wer
 model-index:
-- name: whisper-small-amet
-  results: []
+- name: Whisper Small 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: test+validation
+      args: am_et
+    metrics:
+    - name: Wer
+      type: wer
+      value: 160.99
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 should probably proofread and complete it, then remove this comment. -->
 
-# whisper-small-amet
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fleurs dataset.
+# Whisper Small Tamil FLEURS
+
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the google/fleurs am_et dataset.
+It achieves the following results on the evaluation set:
+- Loss: -
+- Wer: 160.99
 
 ## Model description
 
-More information needed
+- The main Whisper Small Hugging Face page: [Hugging Face - Whisper Small](https://huggingface.co/openai/whisper-small)
 
 ## Intended uses & limitations
 
-More information needed
+- 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.
 
 ## Training and evaluation data
 
-More information needed
+- 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. Num Examples = 446, Num Epochs = 715, Number of trainable parameters = 241734912.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -37,19 +60,88 @@ The following hyperparameters were used during training:
 - train_batch_size: 64
 - eval_batch_size: 32
 - seed: 42
+- gradient_accumulation_steps: 1
+- total_train_batch_size: 64
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
 - training_steps: 1000
 - mixed_precision_training: Native AMP
+- do_eval=False
+
 
 ### Training results
 
+5000 Steps
+
+| Training Loss | Epoch | Step | Learning Rate |
+|:-------------:|:-----:|:-----:|:-----:|
+| 3.0968        | 0.05 | - | 4.2e-7 |
+| 1.178        | 28.57 | - | 3.92e-6 |
+| 0.03        | 53.57 | - | 7.42e-6 |
+| 0.0002        | 217.86 | - | 7.73e-6 |
+| 0.0001        | 378.57 | ~ 2000 | 5.23e-6 |
+| 0.0000        | 382.14 | - | 5.14e-6 |
+| 0.0000        | 467.86 | 3300 | 3.84e-6|
+| 0.0000        | 614.29 | 4300 | 1.56e-6|
+| 0.0000        | 685.71 | 4812 | 4.53e-7|
+| 0.0000        | 710.71 | 4997 | 6.44e-8|
+
+3000 Steps
+
+| Training Loss | Epoch | Step | Learning Rate |
+|:-------------:|:-----:|:-----:|:-----:|
+| 3.0968        | 0.05 | - | 4.2e-7 |
+| 0.0017        | 96.43 | 687 | 9.316e-6 |
+
+
+### 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` only trains and saves the model (if you make changes to `run.sh` be sure to clear/rm the contents as piping appends). 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. 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)
+
+
+### 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}
+}
+
+```
 
 
 ### Framework versions
 
 - Transformers 4.26.0.dev0
-- Pytorch 1.13.1+cu117
+- Pytorch 1.13.0+cu117
 - Datasets 2.7.1.dev0
 - Tokenizers 0.13.2