Merge branch 'main' of https://huggingface.co/tensorops/whisper-medium-th-combined-v2 into main

README.md

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+---
+language:
+- th
+license: apache-2.0
+tags:
+- whisper-event
+- generated_from_trainer
+datasets:
+- mozilla-foundation/common_voice_11_0
+metrics:
+- wer
+model-index:
+- name: Whisper Medium Thai Combined V2 - biodatlab
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: mozilla-foundation/common_voice_11_0 th
+      type: mozilla-foundation/common_voice_11_0
+      config: th
+      split: test
+      args: th
+    metrics:
+    - name: Wer
+      type: wer
+      value: 13.03
+---
+
+<!-- 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 V2
+
+This model is a fine-tuned version of [biodatlab/whisper-medium-th-1000iter](https://huggingface.co/biodatlab/whisper-medium-th-1000iter) on the mozilla-foundation/common_voice_11_0 th dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1475
+- WER: 13.03
+
+## Model description
+
+Use the model with huggingface's `transformers` as follows:
+
+```py
+from transformers import pipeline
+
+MODEL_NAME = "biodatlab/whisper-medium-th-combined-v2"  # 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: 32
+- eval_batch_size: 32
+- 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: 5000
+- 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 4.26.0.dev0
+- Pytorch 1.13.0
+- Datasets 2.7.1
+- Tokenizers 0.13.2