Update README.md

README.md

@@ -5,31 +5,29 @@ tags:
 metrics:
 - wer
 model-index:
-- name: whisper-large-v2-japanese-24h
+- name: whisper-large-v2-japanese-5k-steps
   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-large-v2-japanese-24h
+# whisper-large-v2-japanese-5k-steps
 
-This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
+This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Japanese CommonVoice dataset (v11)..
 It achieves the following results on the evaluation set:
 - Loss: 0.4200
 - Wer: 0.7449
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
+This model is finetuned for 5000 steps for research purposes which means that the transcriptions might not be that satisfactory for users.
 
 ## Training and evaluation data
 
-More information needed
+Training Data: CommonVoice (v11) train split
+Validation Data: CommonVoice (v11) Validation split
+Test Data: CommonVoice (v11) Test split
 
 ## Training procedure
 
@@ -56,6 +54,37 @@ The following hyperparameters were used during training:
 | 0.0002        | 30.53 | 4000 | 0.4123          | 0.7443 |
 | 0.0002        | 38.17 | 5000 | 0.4200          | 0.7449 |
 
+### Transcription
+
+```python
+from datasets import load_dataset, Audio
+import torch
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+
+# device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# load the model
+processor = WhisperProcessor.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps")
+model = WhisperForConditionalGeneration.from_pretrained("clu-ling/whisper-large-v2-arabic-5k-steps").to(device)
+forced_decoder_ids = processor.get_decoder_prompt_ids(language="ja", task="transcribe")
+
+# load the dataset
+commonvoice_eval = load_dataset("mozilla-foundation/common_voice_11_0", "ja", split="validation", streaming=True)
+commonvoice_eval = commonvoice_eval.cast_column("audio", Audio(sampling_rate=16000))
+sample = next(iter(commonvoice_eval))["audio"]
+
+# features and generate token ids
+input_features = processor(sample["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
+predicted_ids = model.generate(input_features.to(device), forced_decoder_ids=forced_decoder_ids)
+
+# decode
+transcription = processor.batch_decode(predicted_ids)
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+
+print(transcription)
+
+```
 
 ### Framework versions