lint. (#266)

examples/interactive_asr/vad.py

@@ -19,18 +19,16 @@ from speech to silence or vice versa.
 
 from collections import deque
 
-import librosa
 import numpy as np
 import torch
+from six.moves import queue
 
+import librosa
 import pyaudio
 import torchaudio
-from six.moves import queue
 
 
 def compute_spectral_flatness(frame, epsilon=0.01):
-    n = frame.nonzero().size(0)
-
     # epsilon protects against log(0)
     geometric_mean = torch.exp((frame + epsilon).log().mean(-1)) - epsilon
     arithmetic_mean = frame.mean(-1)
@@ -240,8 +238,6 @@ def get_microphone_chunks(
 ):
 
     vad = VoiceActivityDetection()
-    speech_frames = []
-    chunks = []
 
     cumulated = []
     precumulated = deque(maxlen=precumulate)
@@ -250,7 +246,6 @@ def get_microphone_chunks(
         audio_generator = stream.generator()
         chunk_length = stream._chunk
         waveform = torch.zeros(max_to_visualize * chunk_length)
-        speechform = torch.zeros(max_to_visualize * chunk_length)
 
         for chunk in audio_generator:
             # Is speech?

examples/test/test_interactive_asr.py

@@ -11,44 +11,93 @@ class ASRTest(unittest.TestCase):
     logger.setLevel(logging.INFO)
 
     arguments_dict = {
-        'path': '/scratch/jamarshon/downloads/model.pt',
-        'input_file': '/scratch/jamarshon/audio/examples/interactive_asr/data/sample.wav',
-        'data': '/scratch/jamarshon/downloads',
-        'user_dir': '/scratch/jamarshon/fairseq-py/examples/speech_recognition',
-        'no_progress_bar': False, 'log_interval': 1000, 'log_format': None,
-        'tensorboard_logdir': '', 'tbmf_wrapper': False, 'seed': 1, 'cpu': True,
-        'fp16': False, 'memory_efficient_fp16': False, 'fp16_init_scale': 128,
-        'fp16_scale_window': None, 'fp16_scale_tolerance': 0.0,
-        'min_loss_scale': 0.0001, 'threshold_loss_scale': None,
-        'criterion': 'cross_entropy', 'tokenizer': None, 'bpe': None, 'optimizer':
-        'nag', 'lr_scheduler': 'fixed', 'task': 'speech_recognition', 'num_workers': 0,
-        'skip_invalid_size_inputs_valid_test': False, 'max_tokens': 10000000,
-        'max_sentences': None, 'required_batch_size_multiple': 8, 'dataset_impl': None,
-        'gen_subset': 'test', 'num_shards': 1, 'shard_id': 0,
-        'remove_bpe': None, 'quiet': False, 'model_overrides': '{}',
-        'results_path': None, 'beam': 40, 'nbest': 1, 'max_len_a': 0,
-        'max_len_b': 200, 'min_len': 1, 'match_source_len': False,
-        'no_early_stop': False, 'unnormalized': False, 'no_beamable_mm': False,
-        'lenpen': 1, 'unkpen': 0, 'replace_unk': None, 'sacrebleu': False,
-        'score_reference': False, 'prefix_size': 0, 'no_repeat_ngram_size': 0,
-        'sampling': False, 'sampling_topk': -1, 'sampling_topp': -1.0,
-        'temperature': 1.0, 'diverse_beam_groups': -1, 'diverse_beam_strength': 0.5,
-        'print_alignment': False, 'ctc': False,
-        'rnnt': False, 'kspmodel': None, 'wfstlm': None, 'rnnt_decoding_type': 'greedy',
-        'lm_weight': 0.2, 'rnnt_len_penalty': -0.5, 'momentum': 0.99, 'weight_decay': 0.0,
-        'force_anneal': None, 'lr_shrink': 0.1, 'warmup_updates': 0}
-
-    arguments_dict['path'] = os.environ.get('ASR_MODEL_PATH', None)
-    arguments_dict['input_file'] = os.environ.get('ASR_INPUT_FILE', None)
-    arguments_dict['data'] = os.environ.get('ASR_DATA_PATH', None)
-    arguments_dict['user_dir'] = os.environ.get('ASR_USER_DIR', None)
+        "path": "/scratch/jamarshon/downloads/model.pt",
+        "input_file": "/scratch/jamarshon/audio/examples/interactive_asr/data/sample.wav",
+        "data": "/scratch/jamarshon/downloads",
+        "user_dir": "/scratch/jamarshon/fairseq-py/examples/speech_recognition",
+        "no_progress_bar": False,
+        "log_interval": 1000,
+        "log_format": None,
+        "tensorboard_logdir": "",
+        "tbmf_wrapper": False,
+        "seed": 1,
+        "cpu": True,
+        "fp16": False,
+        "memory_efficient_fp16": False,
+        "fp16_init_scale": 128,
+        "fp16_scale_window": None,
+        "fp16_scale_tolerance": 0.0,
+        "min_loss_scale": 0.0001,
+        "threshold_loss_scale": None,
+        "criterion": "cross_entropy",
+        "tokenizer": None,
+        "bpe": None,
+        "optimizer": "nag",
+        "lr_scheduler": "fixed",
+        "task": "speech_recognition",
+        "num_workers": 0,
+        "skip_invalid_size_inputs_valid_test": False,
+        "max_tokens": 10000000,
+        "max_sentences": None,
+        "required_batch_size_multiple": 8,
+        "dataset_impl": None,
+        "gen_subset": "test",
+        "num_shards": 1,
+        "shard_id": 0,
+        "remove_bpe": None,
+        "quiet": False,
+        "model_overrides": "{}",
+        "results_path": None,
+        "beam": 40,
+        "nbest": 1,
+        "max_len_a": 0,
+        "max_len_b": 200,
+        "min_len": 1,
+        "match_source_len": False,
+        "no_early_stop": False,
+        "unnormalized": False,
+        "no_beamable_mm": False,
+        "lenpen": 1,
+        "unkpen": 0,
+        "replace_unk": None,
+        "sacrebleu": False,
+        "score_reference": False,
+        "prefix_size": 0,
+        "no_repeat_ngram_size": 0,
+        "sampling": False,
+        "sampling_topk": -1,
+        "sampling_topp": -1.0,
+        "temperature": 1.0,
+        "diverse_beam_groups": -1,
+        "diverse_beam_strength": 0.5,
+        "print_alignment": False,
+        "ctc": False,
+        "rnnt": False,
+        "kspmodel": None,
+        "wfstlm": None,
+        "rnnt_decoding_type": "greedy",
+        "lm_weight": 0.2,
+        "rnnt_len_penalty": -0.5,
+        "momentum": 0.99,
+        "weight_decay": 0.0,
+        "force_anneal": None,
+        "lr_shrink": 0.1,
+        "warmup_updates": 0,
+    }
+
+    arguments_dict["path"] = os.environ.get("ASR_MODEL_PATH", None)
+    arguments_dict["input_file"] = os.environ.get("ASR_INPUT_FILE", None)
+    arguments_dict["data"] = os.environ.get("ASR_DATA_PATH", None)
+    arguments_dict["user_dir"] = os.environ.get("ASR_USER_DIR", None)
     args = argparse.Namespace(**arguments_dict)
 
     def test_transcribe_file(self):
         task, generator, models, sp, tgt_dict = setup_asr(self.args, self.logger)
-        _, transcription = transcribe_file(self.args, task, generator, models, sp, tgt_dict)
+        _, transcription = transcribe_file(
+            self.args, task, generator, models, sp, tgt_dict
+        )
 
-        expected_transcription = [['THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG']]
+        expected_transcription = [["THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG"]]
         self.assertEqual(transcription, expected_transcription, msg=str(transcription))
 
 

torchaudio/datasets/vctk.py

@@ -53,7 +53,6 @@ def read_audio(fp, downsample=True):
 def load_txts(dir):
     """Create a dictionary with all the text of the audio transcriptions."""
     utterences = dict()
-    txts = []
     dir = os.path.expanduser(dir)
     for target in sorted(os.listdir(dir)):
         d = os.path.join(dir, target)