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antony66
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Model Details
Model Provider
antony66
Model Tree
Base
this model
Input Modalities
Audio
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
In order to process phone calls it is highly recommended that you preprocess your records and adjust volume before performing ASR. For example, like this:
bash
sox record.wav -r 16k record-normalized.wav norm -0.5 compand 0.3,1 -90,-90,-70,-70,-60,-20,0,0 -5 0 0.2
Then your ASR code should look somewhat like this:
python
import torchfrom transformers import WhisperForConditionalGeneration, WhisperProcessor, pipelinetorch_dtype = torch.bfloat16 # set your preferred type heredevice = 'cpu'if torch.cuda.is_available():device = 'cuda'elif torch.backends.mps.is_available():device = 'mps'setattr(torch.distributed, "is_initialized", lambda : False) # monkey patchingdevice = 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. For example:from io import BufferIOwav = BytesIO()with open('record-normalized.wav', 'rb') as f:wav.write(f.read())wav.seek(0)# get the transcriptionasr = asr_pipeline(wav, generate_kwargs={"language": "russian", "max_new_tokens": 256}, return_timestamps=False)print(asr['text'])
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.