Delete transcribe_speech.py

transcribe_speech.py

@@ -1,173 +0,0 @@
-# Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#	 http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import contextlib
-import glob
-import json
-import os
-from dataclasses import dataclass
-from typing import Optional
-
-import pytorch_lightning as pl
-import torch
-from omegaconf import OmegaConf
-
-from nemo.collections.asr.metrics.rnnt_wer import RNNTDecodingConfig
-from nemo.collections.asr.metrics.wer import word_error_rate
-from nemo.collections.asr.models import ASRModel
-from nemo.core.config import hydra_runner
-from nemo.utils import logging, model_utils
-
-
-"""
-# Transcribe audio
-# Arguments
-# model_path: path to .nemo ASR checkpoint
-# pretrained_name: name of pretrained ASR model (from NGC registry)
-# audio_dir: path to directory with audio files
-# dataset_manifest: path to dataset JSON manifest file (in NeMo format)
-#
-# ASR model can be specified by either "model_path" or "pretrained_name".
-# Data for transcription can be defined with either "audio_dir" or "dataset_manifest".
-# Results are returned in a JSON manifest file.
-
-python transcribe_speech.py \
-	model_path=null \
-	pretrained_name=null \
-	audio_dir="" \
-	dataset_manifest="" \
-	output_filename=""
-"""
-
-
-@dataclass
-class TranscriptionConfig:
-	# Required configs
-	model_path: Optional[str] = None  # Path to a .nemo file
-	pretrained_name: Optional[str] = None  # Name of a pretrained model
-	audio_dir: Optional[str] = None  # Path to a directory which contains audio files
-	dataset_manifest: Optional[str] = None  # Path to dataset's JSON manifest
-
-	# General configs
-	output_filename: Optional[str] = None
-	batch_size: int = 32
-	cuda: Optional[bool] = None  # will switch to cuda if available, defaults to CPU otherwise
-	amp: bool = False
-	audio_type: str = "wav"
-
-	# decoding strategy for RNNT models
-	rnnt_decoding: RNNTDecodingConfig = RNNTDecodingConfig()
-
-
-@hydra_runner(config_name="TranscriptionConfig", schema=TranscriptionConfig)
-def main(cfg: TranscriptionConfig):
-	logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
-
-	if cfg.model_path is None and cfg.pretrained_name is None:
-		raise ValueError("Both cfg.model_path and cfg.pretrained_name cannot be None!")
-	if cfg.audio_dir is None and cfg.dataset_manifest is None:
-		raise ValueError("Both cfg.audio_dir and cfg.dataset_manifest cannot be None!")
-
-	# setup GPU
-	if cfg.cuda is None:
-		cfg.cuda = torch.cuda.is_available()
-
-	if type(cfg.cuda) == int:
-		device_id = int(cfg.cuda)
-	else:
-		device_id = 0
-
-	device = torch.device(f'cuda:{device_id}' if cfg.cuda else 'cpu')
-
-	# setup model
-	if cfg.model_path is not None:
-		# restore model from .nemo file path
-		model_cfg = ASRModel.restore_from(restore_path=cfg.model_path, return_config=True)
-		classpath = model_cfg.target  # original class path
-		imported_class = model_utils.import_class_by_path(classpath)  # type: ASRModel
-		logging.info(f"Restoring model : {imported_class.__name__}")
-		asr_model = imported_class.restore_from(restore_path=cfg.model_path, map_location=device)  # type: ASRModel
-		model_name = os.path.splitext(os.path.basename(cfg.model_path))[0]
-	else:
-		# restore model by name
-		asr_model = ASRModel.from_pretrained(model_name=cfg.pretrained_name, map_location=device)  # type: ASRModel
-		model_name = cfg.pretrained_name
-
-	trainer = pl.Trainer(gpus=int(cfg.cuda))
-	asr_model.set_trainer(trainer)
-	asr_model = asr_model.eval()
-
-	# Setup decoding strategy
-	if hasattr(asr_model, 'change_decoding_strategy'):
-		asr_model.change_decoding_strategy(cfg.rnnt_decoding)
-
-	# get audio filenames
-	if cfg.audio_dir is not None:
-		filepaths = list(glob.glob(os.path.join(cfg.audio_dir, f"*.{cfg.audio_type}")))
-	else:
-		# get filenames from manifest
-		filepaths = []
-		references = []
-		with open(cfg.dataset_manifest, 'r', encoding='utf-8') as f:
-			for line in f:
-				item = json.loads(line)
-				filepaths.append(item['audio_filepath'])
-				references.append(item['text'])
-	logging.info(f"\nTranscribing {len(filepaths)} files...\n")
-
-	# setup AMP (optional)
-	if cfg.amp and torch.cuda.is_available() and hasattr(torch.cuda, 'amp') and hasattr(torch.cuda.amp, 'autocast'):
-		logging.info("AMP enabled!\n")
-		autocast = torch.cuda.amp.autocast
-	else:
-
-		@contextlib.contextmanager
-		def autocast():
-			yield
-
-	# transcribe audio
-	with autocast():
-		with torch.no_grad():
-			transcriptions = asr_model.transcribe(filepaths, batch_size=cfg.batch_size)
-	logging.info(f"Finished transcribing {len(filepaths)} files !")
-
-	wer_value = word_error_rate(hypotheses=transcriptions, references=references, use_cer=False)
-	logging.info(f'Got WER of {wer_value}. Tolerance was 1.0')
-
-	if cfg.output_filename is None:
-		# create default output filename
-		if cfg.audio_dir is not None:
-			cfg.output_filename = os.path.dirname(os.path.join(cfg.audio_dir, '.')) + '.json'
-		else:
-			cfg.output_filename = cfg.dataset_manifest.replace('.json', f'_{model_name}.json')
-
-	logging.info(f"Writing transcriptions into file: {cfg.output_filename}")
-
-	with open(cfg.output_filename, 'w', encoding='utf-8') as f:
-		if cfg.audio_dir is not None:
-			for idx, text in enumerate(transcriptions):
-				item = {'audio_filepath': filepaths[idx], 'pred_text': text}
-				f.write(json.dumps(item) + "\n")
-		else:
-			with open(cfg.dataset_manifest, 'r', encoding='utf-8') as fr:
-				for idx, line in enumerate(fr):
-					item = json.loads(line)
-					item['pred_text'] = transcriptions[idx]
-					f.write(json.dumps(item, ensure_ascii=False) + "\n")
-
-	logging.info("Finished writing predictions !")
-
-
-if __name__ == '__main__':
-	main()  # noqa pylint: disable=no-value-for-parameter