whisper-large-v3-russian-ties-podlodka-v1.2/README.md

base_model, language, library_name, tags, datasets, metrics

base_model	language	library_name	tags	datasets	metrics
antony66/whisper-large-v3-russian bond005/whisper-large-v3-ru-podlodka	ru	transformers	asr whisper russian mergekit merge	mozilla-foundation/common_voice_17_0 bond005/taiga_speech_v2 bond005/podlodka_speech bond005/rulibrispeech	wer

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 BufferIO
wav = BytesIO()
with open('record-normalized.wav', 'rb') as f:
    wav.write(f.read())
wav.seek(0)

# get the transcription
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.