Update README.md

README.md

@@ -20,6 +20,49 @@ Dataset used for finetuning is Common Voice 11.0, Russian part.
 
 After preprocessing of the original dataset (train + test + validation splits were mixed and split to a new train + test split by 0.95/0.05) the original Whisper v3 has WER 9.2 while the finetuned version shows 6.31 (so far).
 
+## Usage
+
+```
+import torch
+from transformers import WhisperForConditionalGeneration, WhisperProcessor, pipeline
+
+torch_dtype = torch.bfloat16 # set your preferred type here 
+
+device = 'cpu'
+if torch.cuda.is_available():
+    device = 'cuda'
+elif torch.backends.mps.is_available():
+    device = 'mps'
+    setattr(torch.distributed, "is_initialized", lambda : False) # monkey patching
+device = torch.device(device)
+
+whisper = WhisperForConditionalGeneration.from_pretrained(
+    "antony66/whisper-large-v3-russian", torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True,
+    # add attn_implementation="flash_attention_2" if your GPU supports it
+)
+
+processor = WhisperProcessor.from_pretrained("antony66/whisper-large-v3-russian")
+
+asr_pipeline = pipeline(
+    "automatic-speech-recognition",
+    model=whisper,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    max_new_tokens=256,
+    chunk_length_s=30,
+    batch_size=16,
+    return_timestamps=True,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+# read your wav file into variable wav
+asr = asr_pipeline(wav, generate_kwargs={"language": "russian", "max_new_tokens": 256}, return_timestamps=False)
+
+print(asr['text'])
+
+```
+
 ## Work in progress
 
 This model is in WIP state for now. The goal is to finetune it for speech recognition of phone calls as much as possible. If you want to contribute and you know or have any good dataset please let me know. Your help will be much appreciated.