Update README.md

README.md

@@ -1,3 +1,57 @@
----
-license: cc-by-sa-4.0
----
+---
+license: cc-by-4.0
+language:
+- hak
+pipeline_tag: automatic-speech-recognition
+---
+# Model Card for whisper-large-v3-taiwanese-hakka
+
+<!-- Provide a quick summary of what the model is/does. -->
+This model is a early fine-tuned version of the Taiwanese indigenous [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3), which uses the ids of each dialect as prompts during training.
+Note: we use indonesian as whisper language id
+
+## Dialect and Id
+- 阿美語: ami
+- 賽德克語: sdq
+- 太魯閣語: trv
+
+### Training process
+The training of the model was performed with the following hyperparameters
+
+- Batch size: 32
+- Epochs: 4
+- Warmup Steps: 1170
+- Total Steps: 11700
+- Learning rate: 7e-5
+- Data augmentation: No
+
+
+### How to use
+
+```python
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+model_id = "formospeech/whisper-large-v3-formosan-iso-prompt"
+dialect_id = "ami"
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+processor = AutoProcessor.from_pretrained(model_id)
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=128,
+    chunk_length_s=30,
+    batch_size=16,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+generate_kwargs = {"language": "id", "prompt_ids": torch.from_numpy(processor.get_prompt_ids(dialect_id)).to(device)}
+transcription = pipe("path/to/my_audio.wav", generate_kwargs=generate_kwargs)
+print(transcription.replace(f" {dialect_id}", ""))
+```