Add model usage instructions

README.md

@@ -37,6 +37,46 @@ For training, three datasets were used:
 - 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
 
+## Using the Model
+```
+import librosa
+
+import torch
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+
+y, sr = librosa.load('audio.mp3', sr=16000)
+
+MODEL_NAME = "alvanlii/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/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"]
+```
 ## Training Hyperparameters
 - learning_rate: 5e-5
 - train_batch_size: 25 (on 2 GPUs)