update model card README.md

README.md

@@ -1,40 +1,20 @@
 ---
 license: apache-2.0
 tags:
-- whisper-event
 - generated_from_trainer
 datasets:
-- google/fleurs
-metrics:
-- wer
+- fleurs
 model-index:
-- 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: 
+- name: whisper-small-amet
+  results: []
 ---
 
 <!-- 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
 
-# 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: 0.5390 (Validation Loss)
-- Wer: 20.9327 (WER)
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the fleurs dataset.
 
 ## Model description
 
@@ -46,12 +26,10 @@ More information needed
 
 ## 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).
+More information needed
 
 ## 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:
@@ -59,75 +37,19 @@ The following hyperparameters were used during training:
 - train_batch_size: 64
 - eval_batch_size: 32
 - seed: 42
-- gradient_accumulation_steps: 2
-- 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: 5000
+- training_steps: 1000
 - mixed_precision_training: Native AMP
-- do_eval=False
-
 
 ### Training results
 
-| Training Loss | Epoch | Step |
-|:-------------:|:-----:|:-----:|
-| 3.0968        | 3.57 | - |
-| 1.178        | 28.57 | - |
-| 0.03        | 53.57 | - |
-| 0.0002        | 217.86 | - |
-| 0.0001        | 378.57 | ~ 2000 |
-| 0.0000        | 382.14 | - |
-| 0.0000        | 467.86 | 3300 |
-
-
-### 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. 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 save the model. Then run `python run_eval_whisper_streaming.py --model_id="openai/whisper-small" --dataset="google/fleurs" --config="am_et" --device=0 --language="am"` to find the WER score. Erroring out during evaluation prevents the trained model from loading to HugginFace. 
-
-### 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. 
-
-- __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__: Virginia/India
-- __Carbon Emitted__: 14.8 kg
-
-
-### 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}
-}
-
-```
 
 
 ### Framework versions
 
 - Transformers 4.26.0.dev0
-- Pytorch 1.13.0+cu117
+- Pytorch 1.13.1+cu117
 - Datasets 2.7.1.dev0
 - Tokenizers 0.13.2