update model to latest

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,111 @@
+---
+language:
+- th
+license: apache-2.0
+library_name: transformers
+tags:
+- whisper-event
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_13_0
+- google/fleurs
+metrics:
+- wer
+base_model: openai/whisper-medium
+model-index:
+- name: Whisper Medium Thai Combined V4 - biodatlab
+  results:
+  - task:
+      type: automatic-speech-recognition
+      name: Automatic Speech Recognition
+    dataset:
+      name: mozilla-foundation/common_voice_13_0 th
+      type: mozilla-foundation/common_voice_13_0
+      config: th
+      split: test
+      args: th
+    metrics:
+    - type: wer
+      value: 7.42
+      name: Wer
+---
+
+<!-- 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 Medium (Thai): Combined V3
+
+This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on augmented versions of the mozilla-foundation/common_voice_13_0 th, google/fleurs, and curated datasets.
+It achieves the following results on the common-voice-13 test set:
+- WER: 7.42 (with Deepcut Tokenizer)
+
+## Model description
+
+Use the model with huggingface's `transformers` as follows:
+
+```py
+from transformers import pipeline
+
+MODEL_NAME = "biodatlab/whisper-th-medium-combined"  # specify the model name
+lang = "th"  # change to Thai langauge
+
+device = 0 if torch.cuda.is_available() else "cpu"
+
+pipe = pipeline(
+    task="automatic-speech-recognition",
+    model=MODEL_NAME,
+    chunk_length_s=30,
+    device=device,
+)
+pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(
+  language=lang,
+  task="transcribe"
+)
+text = pipe("audio.mp3")["text"] # give audio mp3 and transcribe text
+```
+
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 1e-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- training_steps: 10000
+- mixed_precision_training: Native AMP
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.0
+- Datasets 2.16.1
+- Tokenizers 0.15.1
+
+## Citation
+
+Cite using Bibtex:
+
+```
+@misc {thonburian_whisper_med,
+    author       = { Atirut Boribalburephan, Zaw Htet Aung, Knot Pipatsrisawat, Titipat Achakulvisut },
+    title        = { Thonburian Whisper: A fine-tuned Whisper model for Thai automatic speech recognition },
+    year         = 2022,
+    url          = { https://huggingface.co/biodatlab/whisper-th-medium-combined },
+    doi          = { 10.57967/hf/0226 },
+    publisher    = { Hugging Face }
+}
+```

README.md

@@ -13,20 +13,20 @@ metrics:
 - wer
 base_model: openai/whisper-medium
 model-index:
-- name: Whisper Medium Thai Combined V3 - biodatlab
+- name: Whisper Medium Thai Combined V4 - biodatlab
   results:
   - task:
       type: automatic-speech-recognition
       name: Automatic Speech Recognition
     dataset:
-      name: mozilla-foundation/common_voice_11_0 th
-      type: mozilla-foundation/common_voice_11_0
+      name: mozilla-foundation/common_voice_13_0 th
+      type: mozilla-foundation/common_voice_13_0
       config: th
       split: test
       args: th
     metrics:
     - type: wer
-      value: 8.44
+      value: 7.42
       name: Wer
 ---
 
@@ -36,10 +36,8 @@ should probably proofread and complete it, then remove this comment. -->
 # Whisper Medium (Thai): Combined V3
 
 This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on augmented versions of the mozilla-foundation/common_voice_13_0 th, google/fleurs, and curated datasets.
-It achieves the following results (NOT-UP-TO-DATE) on the common-voice-11 evaluation set:
-- Loss: 0.1475
-- WER: 13.03 (without Tokenizer)
-- WER: 8.44 (with Deepcut Tokenizer)
+It achieves the following results on the common-voice-13 test set:
+- WER: 7.42 (with Deepcut Tokenizer)
 
 ## Model description
 
@@ -81,28 +79,21 @@ More information needed
 
 The following hyperparameters were used during training:
 - learning_rate: 1e-05
-- train_batch_size: 32
-- eval_batch_size: 32
+- train_batch_size: 16
+- eval_batch_size: 16
 - seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- training_steps: 5000
+- training_steps: 10000
 - mixed_precision_training: Native AMP
 
-### Training results
-
-| Training Loss | Epoch | Step | Validation Loss | Wer     |
-|:-------------:|:-----:|:----:|:---------------:|:-------:|
-| 0.0679        | 2.09  | 5000 | 0.1475          | 13.03 |
-
-
 ### Framework versions
 
-- Transformers 4.31.0.dev0
+- Transformers 4.37.2
 - Pytorch 2.1.0
-- Datasets 2.13.1
-- Tokenizers 0.13.3
+- Datasets 2.16.1
+- Tokenizers 0.15.1
 
 ## Citation
 

added_tokens.json

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config.json

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eval_results.json

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generation_config.json

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