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Model: Apel-sin/whisper-large-v3-russian-ties-podlodka-v1.2 Source: Original Platform
2026-05-13 21:08:32 +08:00
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--- a/README.md
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 ---
 base_model:
 - antony66/whisper-large-v3-russian
 - bond005/whisper-large-v3-ru-podlodka
 language:
 - ru
 library_name: transformers
 tags:
 - asr
 - whisper
 - russian
 - mergekit
 - merge
 datasets:
 - mozilla-foundation/common_voice_17_0
 - bond005/taiga_speech_v2
 - bond005/podlodka_speech
 - bond005/rulibrispeech
 metrics:
 - wer
 ---
 # Model Details
 This model was merged using the TIES merge method.
 ```yaml
 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:
 ```bash
 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:
 ```python
 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.
--- a/added_tokens.json
+++ b/added_tokens.json
--- a/config.json
+++ b/config.json
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 {
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 }
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--- a/merges.txt
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--- a/preprocessor_config.json
+++ b/preprocessor_config.json
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+++ b/special_tokens_map.json
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 }
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
--- a/vocab.json
+++ b/vocab.json