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import os
from peft import PeftModel, PeftConfig
import torch
from torch.cuda.amp import autocast
from torch.utils.data import DataLoader
from tqdm import tqdm
import transformers
from transformers import WhisperFeatureExtractor, WhisperTokenizer, WhisperProcessor, WhisperForConditionalGeneration, GenerationConfig
from transformers import pipeline, AutomaticSpeechRecognitionPipeline
import argparse
import time
from pathlib import Path
import json
import pandas as pd
import csv
def prepare_pipeline(model_type='large-v2',
model_dir="../models/whisat-1.2/",
use_stock_model=False,
generate_opts={'max_new_tokens':112,
'num_beams':1,
'repetition_penalty':1,
'do_sample':False}
):
#%% options (TODO make these CLI options)
lang='english'
USE_INT8 = False
import warnings
warnings.filterwarnings("ignore")
transformers.utils.logging.set_verbosity_error()
init_from_hub_path = f"openai/whisper-{model_type}" # TODO infer automatically from PEFT checkpoint
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(device)
feature_extractor = WhisperFeatureExtractor.from_pretrained(init_from_hub_path)
# TODO: no need to specify lanf/task?
tokenizer = WhisperTokenizer.from_pretrained(init_from_hub_path, language=lang, task="transcribe")
processor = WhisperProcessor.from_pretrained(init_from_hub_path, language=lang, task="transcribe")
if use_stock_model:
model =WhisperForConditionalGeneration.from_pretrained(init_from_hub_path)
else:
checkpoint_dir = os.path.expanduser(model_dir)
# check if PEFT
if os.path.isdir(os.path.join(checkpoint_dir , "adapter_model")):
print('...it looks like this model was tuned using PEFT, because adapter_model/ is present in ckpt dir')
# checkpoint dir needs adapter model subdir with adapter_model.bin and adapter_confg.json
peft_config = PeftConfig.from_pretrained(os.path.join(checkpoint_dir , "adapter_model"))
# except ValueError as e: # if final checkpoint these are in the parent checkpoint direcory
# peft_config = PeftConfig.from_pretrained(os.path.join(checkpoint_dir ), subfolder=None)
model = WhisperForConditionalGeneration.from_pretrained(peft_config.base_model_name_or_path,
load_in_8bit=USE_INT8,
device_map='auto',
use_cache=False,
)
model = PeftModel.from_pretrained(model, os.path.join(checkpoint_dir,"adapter_model"))
else:
model = WhisperForConditionalGeneration.from_pretrained(checkpoint_dir,
load_in_8bit=USE_INT8,
device_map='auto',
use_cache=False,
)
model.eval() # needed?
pipe = AutomaticSpeechRecognitionPipeline(
# task="automatic-speech-recognition",
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
chunk_length_s=30,
device=device,
return_timestamps=False,
generate_kwargs=generate_opts,
)
return(pipe)
def load_model(model_type='large-v2',
model_dir="../models/whisat-1.2/"):
lang='english'
USE_INT8 = False
import warnings
warnings.filterwarnings("ignore")
transformers.utils.logging.set_verbosity_error()
init_from_hub_path = f"openai/whisper-{model_type}" # TODO infer automatically from PEFT checkpoint
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
feature_extractor = WhisperFeatureExtractor.from_pretrained(init_from_hub_path)
# TODO: no need to specify lanf/task?
tokenizer = WhisperTokenizer.from_pretrained(init_from_hub_path, language=lang, task="transcribe")
processor = WhisperProcessor.from_pretrained(init_from_hub_path, language=lang, task="transcribe")
checkpoint_dir = os.path.expanduser(model_dir)
# checkpoint dir needs adapter model subdir with adapter_model.bin and adapter_confg.json
peft_config = PeftConfig.from_pretrained(os.path.join(checkpoint_dir , "adapter_model"))
# except ValueError as e: # if final checkpoint these are in the parent checkpoint direcory
# peft_config = PeftConfig.from_pretrained(os.path.join(checkpoint_dir ), subfolder=None)
model = WhisperForConditionalGeneration.from_pretrained(peft_config.base_model_name_or_path,
load_in_8bit=USE_INT8, # TODO: seemed slightly better without?
device_map='auto',
use_cache=False,
)
model = PeftModel.from_pretrained(model, os.path.join(checkpoint_dir,"adapter_model"))
model.eval() # needed?
return(model, tokenizer, processor)
def ASRdirWhisat(
audio_dir,
files_to_include=None,
out_dir = '../whisat_results/',
model_type='large-v2',
model_name='whisat-1.2',
model_dir="../models/whisat-1.2",
use_stock_model=False,
max_new_tokens=112,
num_beams=1,
do_sample=False,
repetition_penalty=1,
):
## ASR using fine-tuned Transformers Whisper
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Simply trancsribe each file in the specified folder separately
# Whisper takes 30-second input. Anything shorter than this will be 0 padded. Longer will be concatenated.
# Save output in same directory structure as input in specified top-level folder
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#TODO optional arg listing files to transcribe in a list or a text file
asr_model=prepare_pipeline(
model_type=model_type,
model_dir=model_dir,
use_stock_model=use_stock_model,
generate_opts={'max_new_tokens':max_new_tokens,
'num_beams':num_beams,
'repetition_penalty':repetition_penalty,
'do_sample':do_sample
}
)
if use_stock_model: # set some alternative defaults if using stock model
model_name='whisper_' + model_type + '_stock'
if files_to_include:
assert isinstance(files_to_include,list) ,'files_to_include should be a list of paths relative to audio_dir to transcribe'
audio_files=files_to_include
# audio_files=[]
# 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() )]:
# print(f)
# if os.path.join(audio_dir,f) in files_to_include:
# audio_files.append(f)
# print(f'Including {len(audio_files)} hypotheses matching files_to_include...')
else:
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() )]
# audio_identifier = os.path.basename(audio_dir)
asrDir = os.path.join(out_dir,f'ASR_{model_name}') # Dir where full session asr result will be stored
jsonDir = os.path.join(out_dir,f'JSON_{model_name}')
os.makedirs(asrDir, exist_ok=True)
os.makedirs(jsonDir, exist_ok=True)
message = "This may take a while on CPU. Go make a cuppa" if asr_model.device.type=="cpu" else "Running on GPU"
print(f'Running ASR for {len(audio_files)} files. {message} ...')
compute_time=0
total_audio_dur=0
# get the start time
st = time.time()
for audiofile in tqdm(audio_files):
sessname=Path(audiofile).stem
sesspath=os.path.relpath(os.path.dirname(Path(audiofile).resolve()),Path(audio_dir).resolve())
asrFullFile = os.path.join(asrDir,sesspath,f"{sessname}.asr.txt") # full session ASR results file
jsonFile = os.path.join(jsonDir,sesspath, f"{sessname}.json")
os.makedirs(os.path.join(asrDir,sesspath),exist_ok=True)
os.makedirs(os.path.join(jsonDir,sesspath),exist_ok=True)
with torch.no_grad():
with autocast():
try:
result = asr_model(audiofile)
except ValueError as e:
print(f'{e}: {audiofile}')
continue
# save full result JSON
with open(jsonFile, "w") as jf:
json.dump(result, jf, indent=4)
# save full result transcript
# if asr_model.return_timestamps:
# asrtext = '\n'.join([r['text'].strip() for r in result['chunks']])
# else:
asrtext = result['text']
with open(asrFullFile,'w') as outfile:
outfile.write(asrtext)
# print(asrtext)
et = time.time()
compute_time = (et-st)
print(f'...transcription complete in {compute_time:.1f} sec')
def ASRmanifestWhisat(
manifest_csv,
out_csv,
corpora_root,
model_type='large-v2',
model_dir="../models/whisat-1.2",
use_stock_model=False,
max_new_tokens=112,
num_beams=1,
do_sample=False,
repetition_penalty=1,
):
## ASR using fine-tuned Transformers Whisper
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Simply trancsribe each file in the specified folder separately
# Whisper takes 30-second input. Anything shorter than this will be 0 padded. Longer will be concatenated.
# Save output in same directory structure as input in specified top-level folder
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
df = pd.read_csv(manifest_csv,keep_default_na=False)
fieldnames = list(df.columns) + ['asr']
asr_model=prepare_pipeline(
model_type=model_type,
model_dir=model_dir,
use_stock_model=use_stock_model,
generate_opts={'max_new_tokens':max_new_tokens,
'num_beams':num_beams,
'repetition_penalty':repetition_penalty,
'do_sample':do_sample
}
)
message = "This may take a while on CPU. Go make a cuppa " if asr_model.device.type=="cpu" else "Running on GPU"
print(f'Running ASR for {len(df)} files. {message} ...')
compute_time=0
total_audio_dur=0
# get the start time
st = time.time()
with open(out_csv, 'w', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames,delimiter=',')
writer.writeheader()
for i,row in tqdm(df.iterrows(), total=df.shape[0]):
audiofile=row['wav'].replace('$DATAROOT',corpora_root)
with torch.no_grad():
with autocast():
try:
result = asr_model(audiofile)
asrtext = result['text']
except ValueError as e:
print(f'{e}: {audiofile}')
asrtext=''
row['asr']=asrtext
writer.writerow( row.to_dict())
et = time.time()
compute_time = (et-st)
print(f'...transcription complete in {compute_time:.1f} sec')
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