rosyvs
commited on
Commit
•
e404b97
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Parent(s):
f60eaa1
new READMe, tidy up main and add hparams
Browse files- README.md +35 -7
- hparams.yaml +50 -0
- main.py +25 -194
README.md
CHANGED
@@ -8,12 +8,13 @@ Model trained in int8 with LoRA
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Usage:
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prepare pipeline,
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```
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asr_model=prepare_pipeline(
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model_dir='.', # wherever you save the model
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-
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'num_beams':1,
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'repetition_penalty':1,
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'do_sample':False
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@@ -25,8 +26,35 @@ run ASR:
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asr_model(audio_path)
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```
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Usage:
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prepare pipeline, providing any custom generate_kwargs supprted by https://huggingface.co/docs/transformers/v4.40.0/en/main_classes/text_generation#transformers.GenerationConfig
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```
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asr_model=prepare_pipeline(
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model_dir='.', # wherever you save the model
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generate_kwargs={
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'max_new_tokens':112,
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'num_beams':1,
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'repetition_penalty':1,
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'do_sample':False
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asr_model(audio_path)
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```
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run ASR on full directory in `audio_dir`:
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If generate_kwargs not specified, will give you (deterministic) greedy decoding with up to 112 tokens generated, no repetition penalty
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```
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ASRdirWhisat(
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audio_dir,
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out_dir = '../whisat_results/',
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model_dir=".",
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)
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```
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Training information:
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Training script: tune_hf_whisper.py
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Training hyperparameters: hparams.yaml
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Training data manifest: PUBLIC_KIDS_TRAIN_v4_deduped.csv
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Note: to recreate this training you will need to acquire the following public datasets:
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MyST (myst-v0.4.2)
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CuKids
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CSLU
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and ensure they are stored at paths consistend with those in the data manifest above.
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Reference:
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@inproceedings{southwell2024,
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title={Automatic speech recognition tuned for child speech in the classroom},
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author={ Southwell, Rosy and Ward , Wayne and Trinh , Viet Anh and Clevenger, Charis and Clevenger, Clay and Watts, Emily and Reitman, Jason and D’Mello, Sidney and Whitehill, Jacob},
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booktitle={{IEEE} International Conference on Acoustics, Speech and Signal Processing
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{ICASSP} 2024, Seoul, South Korea, April 14-19, 2024},
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year={2024},
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}
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hparams.yaml
ADDED
@@ -0,0 +1,50 @@
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# parameters to set
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model_cfg:
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init_from_hub_path: openai/whisper-large-v2
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# lang: None
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# apply_spec_augment: True
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# mask_time_prob: 0.05
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# mask_feature_prob: 0.05
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# mask_time_length: 40
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# mask_feature_length: 30
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# mask_time_min_masks: 2
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# mask_feature_min_masks: 2
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data_cfg:
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data_root: ~/corpora/
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train_manif: ~/corpora/data_manifests/ASR/PUBLIC_KIDS_TRAIN_v4_deduped.csv
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val_manif: # small private dataset of classroom speech, only affects training if load_best_model_at_end: True
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test_manif: # small private dataset of classroom speech, doesn't affect training
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experiment_cfg:
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OUT_DIR: train/whisat/save/publicKS_LoRA_int8
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use_lora: True
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use_int8: True
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train_cfg:
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training_args:
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output_dir: !ref <experiment_cfg[OUT_DIR]>
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per_device_train_batch_size: 32 # 64
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learning_rate: 0.0001 # 1e-5 orig, 1e-3 lora
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warmup_steps: 50 # 500 orig 50 lora
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num_train_epochs: 1
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fp16: True # True
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evaluation_strategy: steps # or epochs
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per_device_eval_batch_size: 4
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predict_with_generate: True
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generation_max_length: 112
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save_steps: 500
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eval_steps: 500
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eval_accumulation_steps: 2
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logging_steps: 25
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report_to:
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- tensorboard
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load_best_model_at_end: False
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metric_for_best_model: wer
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greater_is_better: False
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push_to_hub: False
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remove_unused_columns: False # required as the PeftModel forward doesn't have the signature of the wrapped model's forward
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label_names:
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- labels
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main.py
CHANGED
@@ -14,110 +14,30 @@ import json
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import pandas as pd
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import csv
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def prepare_pipeline(
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print(device)
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feature_extractor = WhisperFeatureExtractor.from_pretrained(init_from_hub_path)
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# TODO: no need to specify lanf/task?
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tokenizer = WhisperTokenizer.from_pretrained(init_from_hub_path, language=lang, task="transcribe")
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processor = WhisperProcessor.from_pretrained(init_from_hub_path, language=lang, task="transcribe")
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if use_stock_model:
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model =WhisperForConditionalGeneration.from_pretrained(init_from_hub_path)
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else:
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checkpoint_dir = os.path.expanduser(model_dir)
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# check if PEFT
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if os.path.isdir(os.path.join(checkpoint_dir , "adapter_model")):
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print('...it looks like this model was tuned using PEFT, because adapter_model/ is present in ckpt dir')
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# checkpoint dir needs adapter model subdir with adapter_model.bin and adapter_confg.json
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peft_config = PeftConfig.from_pretrained(os.path.join(checkpoint_dir , "adapter_model"))
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# except ValueError as e: # if final checkpoint these are in the parent checkpoint direcory
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# peft_config = PeftConfig.from_pretrained(os.path.join(checkpoint_dir ), subfolder=None)
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model = WhisperForConditionalGeneration.from_pretrained(peft_config.base_model_name_or_path,
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load_in_8bit=USE_INT8,
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device_map='auto',
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use_cache=False,
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)
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model = PeftModel.from_pretrained(model, os.path.join(checkpoint_dir,"adapter_model"))
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else:
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model = WhisperForConditionalGeneration.from_pretrained(checkpoint_dir,
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load_in_8bit=USE_INT8,
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device_map='auto',
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use_cache=False,
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)
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model.eval() # needed?
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pipe = AutomaticSpeechRecognitionPipeline(
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# task="automatic-speech-recognition",
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model=model,
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tokenizer=tokenizer,
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feature_extractor=feature_extractor,
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chunk_length_s=30,
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device=device,
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return_timestamps=False,
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generate_kwargs=generate_opts,
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)
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return(pipe)
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def load_model(model_type='large-v2',
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model_dir="../models/whisat-1.2/"):
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lang='english'
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USE_INT8 = False
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import warnings
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warnings.filterwarnings("ignore")
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transformers.utils.logging.set_verbosity_error()
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init_from_hub_path = f"openai/whisper-{model_type}" # TODO infer automatically from PEFT checkpoint
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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print(device)
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feature_extractor = WhisperFeatureExtractor.from_pretrained(init_from_hub_path)
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# TODO: no need to specify lanf/task?
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tokenizer = WhisperTokenizer.from_pretrained(init_from_hub_path, language=lang, task="transcribe")
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processor = WhisperProcessor.from_pretrained(init_from_hub_path, language=lang, task="transcribe")
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checkpoint_dir = os.path.expanduser(model_dir)
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# checkpoint dir needs adapter model subdir with adapter_model.bin and adapter_confg.json
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peft_config = PeftConfig.from_pretrained(os.path.join(checkpoint_dir , "adapter_model"))
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# except ValueError as e: # if final checkpoint these are in the parent checkpoint direcory
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# peft_config = PeftConfig.from_pretrained(os.path.join(checkpoint_dir ), subfolder=None)
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model = WhisperForConditionalGeneration.from_pretrained(peft_config.base_model_name_or_path,
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load_in_8bit=USE_INT8, # TODO: seemed slightly better without?
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device_map='auto',
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use_cache=False,
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)
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model = PeftModel.from_pretrained(model, os.path.join(checkpoint_dir,"adapter_model"))
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model.eval() # needed?
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return(model, tokenizer, processor)
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def ASRdirWhisat(
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audio_dir,
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files_to_include=None,
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out_dir = '../whisat_results/',
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model_name='whisat-1.2',
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model_dir="../models/whisat-1.2",
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use_stock_model=False,
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max_new_tokens=112,
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num_beams=1,
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do_sample=False,
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# Save output in same directory structure as input in specified top-level folder
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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#TODO optional arg listing files to transcribe in a list or a text file
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asr_model=prepare_pipeline(
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model_type=model_type,
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model_dir=model_dir,
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use_stock_model=use_stock_model,
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'num_beams':num_beams,
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'repetition_penalty':repetition_penalty,
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'do_sample':do_sample
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}
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)
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if use_stock_model: # set some alternative defaults if using stock model
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model_name='whisper_' + model_type + '_stock'
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if
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assert isinstance(files_to_include,list) ,'files_to_include should be a list of paths relative to audio_dir to transcribe'
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audio_files=files_to_include
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# audio_files=[]
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# for f in [str(f) for f in Path(audio_dir).rglob("*") if (str(f).rsplit('.',maxsplit=1)[-1] in ['MOV', 'mov', 'WAV', 'wav', 'mp4', 'mp3', 'm4a', 'aac', 'flac', 'alac', 'ogg'] and f.is_file() )]:
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# print(f)
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# if os.path.join(audio_dir,f) in files_to_include:
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# audio_files.append(f)
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# print(f'Including {len(audio_files)} hypotheses matching files_to_include...')
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else:
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audio_files = [str(f) for f in Path(audio_dir).rglob("*") if (str(f).rsplit('.',maxsplit=1)[-1] in ['MOV', 'mov', 'WAV', 'wav', 'mp4', 'mp3', 'm4a', 'aac', 'flac', 'alac', 'ogg'] and f.is_file() )]
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# audio_identifier = os.path.basename(audio_dir)
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jsonDir = os.path.join(out_dir,f'JSON_{model_name}')
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os.makedirs(asrDir, exist_ok=True)
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os.makedirs(jsonDir, exist_ok=True)
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message = "This may take a while on CPU.
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print(f'Running ASR for {len(audio_files)} files. {message} ...')
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compute_time=0
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total_audio_dur=0
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# get the start time
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st = time.time()
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for audiofile in tqdm(audio_files):
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sessname=Path(audiofile).stem
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sesspath=os.path.relpath(os.path.dirname(Path(audiofile).resolve()),Path(audio_dir).resolve())
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asrFullFile = os.path.join(asrDir,sesspath,f"{sessname}.asr.txt") # full session ASR results file
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jsonFile = os.path.join(jsonDir,sesspath, f"{sessname}.json")
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os.makedirs(os.path.join(asrDir,sesspath),exist_ok=True)
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os.makedirs(os.path.join(jsonDir,sesspath),exist_ok=True)
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with torch.no_grad():
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with autocast():
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print(f'{e}: {audiofile}')
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continue
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# save full result JSON
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with open(jsonFile, "w") as jf:
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json.dump(result, jf, indent=4)
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# save full result transcript
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# if asr_model.return_timestamps:
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# asrtext = '\n'.join([r['text'].strip() for r in result['chunks']])
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# else:
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asrtext = result['text']
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with open(asrFullFile,'w') as outfile:
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compute_time = (et-st)
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print(f'...transcription complete in {compute_time:.1f} sec')
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def ASRmanifestWhisat(
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manifest_csv,
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out_csv,
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corpora_root,
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model_type='large-v2',
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model_dir="../models/whisat-1.2",
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use_stock_model=False,
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max_new_tokens=112,
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num_beams=1,
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do_sample=False,
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repetition_penalty=1,
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):
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## ASR using fine-tuned Transformers Whisper
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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# Simply trancsribe each file in the specified folder separately
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# Whisper takes 30-second input. Anything shorter than this will be 0 padded. Longer will be concatenated.
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# Save output in same directory structure as input in specified top-level folder
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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df = pd.read_csv(manifest_csv,keep_default_na=False)
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fieldnames = list(df.columns) + ['asr']
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asr_model=prepare_pipeline(
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model_type=model_type,
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model_dir=model_dir,
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use_stock_model=use_stock_model,
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generate_opts={'max_new_tokens':max_new_tokens,
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'num_beams':num_beams,
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'repetition_penalty':repetition_penalty,
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'do_sample':do_sample
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}
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)
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message = "This may take a while on CPU. Go make a cuppa " if asr_model.device.type=="cpu" else "Running on GPU"
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print(f'Running ASR for {len(df)} files. {message} ...')
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compute_time=0
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total_audio_dur=0
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# get the start time
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st = time.time()
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with open(out_csv, 'w', newline='') as csvfile:
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames,delimiter=',')
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writer.writeheader()
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for i,row in tqdm(df.iterrows(), total=df.shape[0]):
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audiofile=row['wav'].replace('$DATAROOT',corpora_root)
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with torch.no_grad():
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with autocast():
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try:
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result = asr_model(audiofile)
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asrtext = result['text']
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except ValueError as e:
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print(f'{e}: {audiofile}')
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asrtext=''
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row['asr']=asrtext
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writer.writerow( row.to_dict())
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et = time.time()
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compute_time = (et-st)
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print(f'...transcription complete in {compute_time:.1f} sec')
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-
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import pandas as pd
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import csv
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+
def prepare_pipeline(model_path, generate_kwargs):
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18 |
+
"""Prepare a pipeline for ASR inference
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19 |
+
Args:
|
20 |
+
model_path (str): path to model directory / huggingface model name
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21 |
+
generate_kwargs (dict): options to pass to pipeline
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22 |
+
Returns:
|
23 |
+
pipeline: ASR pipeline
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24 |
+
"""
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25 |
+
processor = WhisperProcessor.from_pretrained(model_path)
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+
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27 |
+
asr_pipeline = pipeline(
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+
"automatic-speech-recognition",
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29 |
+
model=model_path,
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30 |
+
tokenizer=processor.tokenizer,
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31 |
+
feature_extractor=processor.feature_extractor,
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32 |
+
generate_kwargs=generate_kwargs,
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33 |
+
model_kwargs={"load_in_8bit": False},
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+
device_map='auto')
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+
return asr_pipeline
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36 |
|
37 |
def ASRdirWhisat(
|
38 |
audio_dir,
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|
39 |
out_dir = '../whisat_results/',
|
40 |
+
model_dir=".",
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41 |
max_new_tokens=112,
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42 |
num_beams=1,
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43 |
do_sample=False,
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|
51 |
# Save output in same directory structure as input in specified top-level folder
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52 |
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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53 |
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54 |
|
55 |
asr_model=prepare_pipeline(
|
56 |
model_type=model_type,
|
57 |
model_dir=model_dir,
|
58 |
use_stock_model=use_stock_model,
|
59 |
+
generate_kwargs={'max_new_tokens':max_new_tokens,
|
60 |
'num_beams':num_beams,
|
61 |
'repetition_penalty':repetition_penalty,
|
62 |
'do_sample':do_sample
|
63 |
}
|
64 |
)
|
65 |
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|
66 |
|
67 |
+
audio_files = [str(f) for f in Path(audio_dir).rglob("*") if (str(f).rsplit('.',maxsplit=1)[-1] in ['MOV', 'mov', 'WAV', 'wav', 'mp4', 'mp3', 'm4a', 'aac', 'flac', 'alac', 'ogg'] and f.is_file() )]
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|
68 |
|
69 |
# audio_identifier = os.path.basename(audio_dir)
|
70 |
+
os.makedirs(out_dir, exist_ok=True)
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|
71 |
|
72 |
+
message = "This may take a while on CPU." if asr_model.device.type=="cpu" else "Running on GPU"
|
73 |
print(f'Running ASR for {len(audio_files)} files. {message} ...')
|
74 |
compute_time=0
|
75 |
total_audio_dur=0
|
76 |
# get the start time
|
77 |
st = time.time()
|
78 |
+
asrDir = out_dir
|
79 |
for audiofile in tqdm(audio_files):
|
80 |
sessname=Path(audiofile).stem
|
81 |
sesspath=os.path.relpath(os.path.dirname(Path(audiofile).resolve()),Path(audio_dir).resolve())
|
82 |
asrFullFile = os.path.join(asrDir,sesspath,f"{sessname}.asr.txt") # full session ASR results file
|
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|
83 |
os.makedirs(os.path.join(asrDir,sesspath),exist_ok=True)
|
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|
84 |
|
85 |
with torch.no_grad():
|
86 |
with autocast():
|
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|
90 |
print(f'{e}: {audiofile}')
|
91 |
continue
|
92 |
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|
93 |
asrtext = result['text']
|
94 |
|
95 |
with open(asrFullFile,'w') as outfile:
|
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|
99 |
compute_time = (et-st)
|
100 |
print(f'...transcription complete in {compute_time:.1f} sec')
|
101 |
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