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import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
class Whisper:
"""Whisper - audio transcriber class"""
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
def __init__(self, model_id: str = "openai/whisper-base") -> None:
self.model_id = model_id
self.model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id,
torch_dtype=self.torch_dtype,
low_cpu_mem_usage=True,
use_safetensors=True,
)
self.model.to(self.device)
self.processor = AutoProcessor.from_pretrained(model_id)
@property
def model_name(self):
"""
Getter method for retrieving the model name.
"""
return self.model_id
def save(self, save_dir: str):
"""
Saves the model and processor to the specified directory.
Args:
save_dir (str): The directory where the model and processor will be saved.
"""
self.model.save_pretrained(f"{save_dir}/model")
self.processor.save_pretrained(f"{save_dir}/processor")
def load(self, load_dir: str):
"""
Load the model and processor from the specified directory.
Args:
load_dir (str): The directory from which to load the model and processor.
"""
self.model = AutoModelForSpeechSeq2Seq.from_pretrained(f"{load_dir}/model")
self.processor = AutoProcessor.from_pretrained(f"{load_dir}/processor")
self.model.to(self.device)
def pipeline(self):
pipe = pipeline(
"automatic-speech-recognition",
model=self.model,
tokenizer=self.processor.tokenizer,
feature_extractor=self.processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=15,
batch_size=16,
return_timestamps=True,
torch_dtype=self.torch_dtype,
device=self.device,
)
return pipe
def transcribe_audio(file):
whisper = Whisper()
pipe = whisper.pipeline()
result = pipe(file)
return result
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