whisper large v3 russian ties podlodka v1.2 EU AI Act Compliance Profile

Model Details

This model was merged using the TIES merge method.

method: ties
parameters:
  ties_density: 0.9
  encoder_weights:
    - 0.8
    - 0.2
  decoder_weights:
    - 0.2
    - 0.8
models:
  model_a: "/mnt/cloud/llm/whisper/whisper-large-v3-russian"
  model_b: "/mnt/cloud/llm/whisper/whisper-large-v3-ru-podlodka"
output_dir: "/mnt/cloud/llm/whisper/whisper-large-v3-russian-ties-podlodka"

Simple API server

It can be uses with simple OpenAI compatible API server: https://github.com/kreolsky/whisper-api-server/

Usage

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:

sox record.wav -r 8000 record-normalized.wav norm -0.5 compand 0.3,1 -90,-90,-70,-50,-40,-15,0,0 -7 0 0.15

Then your ASR code should look somewhat like this:

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. For example:
from io import

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Base Model