Create README.md

README.md

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+---
+language:
+- zh
+license: apache-2.0
+tags:
+- whisper-event
+- generated_from_trainer
+base_model: openai/whisper-small
+datasets:
+- mozilla-foundation/common_voice_11_0
+model-index:
+- name: Distil-Whisper Small zh-HK - Alvin
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: mozilla-foundation/common_voice_16_0 yue
+      type: mozilla-foundation/common_voice_16_0
+      config: yue
+      split: test
+      args: yue
+    metrics:
+    - name: Normalized CER
+      type: cer
+      value: 9.77
+---
+<!-- 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. -->
+
+# Distil-Whisper Small zh-HK - Alvin
+
+This model is a distilled and fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Cantonese language. It achieves a 9.77 CER (without punctuations), 11.7 CER (with punctuations) on Common Voice 16.0
+
+## Training and evaluation data
+For training,
+- CantoMap: Winterstein, Grégoire, Tang, Carmen and Lai, Regine (2020) "CantoMap: a Hong Kong Cantonese MapTask Corpus", in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille: European Language Resources Association, p. 2899-2906.
+- Cantonse-ASR: Yu, Tiezheng, Frieske, Rita, Xu, Peng, Cahyawijaya, Samuel, Yiu, Cheuk Tung, Lovenia, Holy, Dai, Wenliang, Barezi, Elham, Chen, Qifeng, Ma, Xiaojuan, Shi, Bertram, Fung, Pascale (2022) "Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset", 2022. Link: https://arxiv.org/pdf/2201.02419.pdf
+- Common Voice yue and zh-HK train sets
+
+For evaluation, Common Voice 16.0 yue Test set is used.
+
+## Results
+- CER (lower is better): 0.117
+- GPU Inference with Fast Attention (example below): 0.039s/sample
+  - Note all GPU evaluations are done on RTX 3090 GPU
+- GPU Inference: <TODO>s/sample
+- CPU Inference: 2.57s/sample
+- GPU VRAM: ~2 GB
+
+
+## Using the Model
+```
+import librosa
+
+import torch
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+
+y, sr = librosa.load('audio.mp3', sr=16000)
+
+MODEL_NAME = "alvanlii/distil-whisper-small-cantonese"
+
+processor = WhisperProcessor.from_pretrained(MODEL_NAME)
+model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME)
+
+model.config.forced_decoder_ids = None
+model.config.suppress_tokens = []
+model.config.use_cache = False
+
+processed_in = processor(y, sampling_rate=sr, return_tensors="pt")
+gout = model.generate(
+    input_features=processed_in.input_features, 
+    output_scores=True, return_dict_in_generate=True
+)
+transcription = processor.batch_decode(gout.sequences, skip_special_tokens=True)[0]
+print(transcription)
+```
+- Alternatively, you can use huggingface pipelines
+```
+from transformers import pipeline
+MODEL_NAME = "alvanlii/distil-whisper-small-cantonese" 
+lang = "zh"
+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(file)["text"]
+```
+
+## Model Speedup
+Just add attn_implementation="sdpa" for Flash Attention. 
+```
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    "alvanlii/distil-whisper-small-cantonese",
+    torch_dtype=torch_dtype,
+    low_cpu_mem_usage=True,
+    use_safetensors=True,
+    attn_implementation="sdpa",
+)
+```
+Using Flash Attention reduced the amount of time taken per sample from <TODO>s to 0.039s.
+