get_tokenizer: !name:whisper.tokenizer.get_tokenizer multilingual: True num_languages: 100 language: 'en' task: 'transcribe' allowed_special: 'all' tokenize: !name:cosyvoice.dataset.processor.tokenize get_tokenizer: !ref <get_tokenizer> allowed_special: !ref <allowed_special> filter: !name:cosy...
tokenizer import Tokenizer from faster_whisper.utils import download_model class Word(NamedTuple): @@ -57,7 +58,7 @@ class TranscriptionOptions(NamedTuple): class WhisperModel: def __init__( self, model_path: str, model_size_or_path: str, device: str = "auto", device_index: Union[int...
Despite using an order of magnitude less data, Canary outperforms the similarly sized Whisper-large-v3, and SeamlessM4T-Medium-v1 models on both transcription and translation tasks. On the MCV 16.1 test sets for English, Spanish, French, and German, Canary had a WER of 5.77 (...
import{tokenizers}from"@lenml/tokenizers";const{CLIPTokenizer,AutoTokenizer,CohereTokenizer,VitsTokenizer,WhisperTokenizer,// ...}=tokenizers; In some cases, you may need to use an older version of Node.js, so you might not be able to use pre-packaged packages. In such situations, you can...
common.dist_utils import download_cached_file from medlvlm.common.utils import get_abs_path, is_url from .vision_model.builder import build_vision_encoder from .text2speech.builder import build_audio_encoder from transformers import AutoTokenizer Expand Down Expand Up @@ -165,6 +166,27 @@ ...
tokenizer = WhisperTokenizer.from_pretrained(model_name_or_path, language=language, task=task) processor = WhisperProcessor.from_pretrained(model_name_or_path, language=language, task=task) model = WhisperForConditionalGeneration.from_pretrained(model_name_or_path, load_in_8bit=True, device_map="...
I confirmed this with CLIPTokenizer. GT-KIM commented Apr 6, 2023 same problem in Windows 10 latest version when I use "from_pretrained("openai/whisper-tiny").". The same code worked 2~3 days ago. LePetitNewbie commented Apr 6, 2023 Not sure if its similar. First time ever using ...
importjaximportjax.numpyasjnpfromnanodlimporttime_rng_keyfromnanodlimportArrayDataset,DataLoaderfromnanodlimportWhisper,WhisperDataParallelTrainer# Dummy data parametersbatch_size=8max_length=50embed_dim=256vocab_size=1000# Generate data: replace with actual tokenised/quantised datadummy_targets=jnp.ones(...
from transformers import AutoModelForCausalLM import numpy as np def replace_key(key: str) -> str: if "wte.weight" in key: key = "wte.weight"1 change: 0 additions & 1 deletion 1 whisper/test.py Original file line numberDiff line numberDiff line change @@ -65,7 +65,6 @@ def ...
# export PYTHONPATH=/root/whisper:$PYTHONPATH export PYTHONPATH=/root/fairseq:$PYTHONPATH # export CUDA_VISIBLE_DEVICES=6,7 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 export TOKENIZERS_PARALLELISM=false # export CUDA_LAUNCH_BLOCKING=1 export OMP_NUM_THREADS=1 # debug setting for multipl...