def __init__(self, dataset_dir, manifest_filepath, labels, featurizer, max_duration=None, pad_to_max=False,
min_duration=None, blank_index=0, max_utts=0, normalize=True, sort_by_duration=False,
trim=False, speed_perturbation=False):
"""Dataset that loads tensors via a json file containing paths to audio files, transcripts, and durations
(in seconds). Each entry is a different audio sample.
Args:
dataset_dir: absolute path to dataset folder
manifest_filepath: relative path from dataset folder to manifest json as described above. Can be coma-separated paths.
labels: String containing all the possible characters to map to
featurizer: Initialized featurizer class that converts paths of audio to feature tensors
max_duration: If audio exceeds this length, do not include in dataset
min_duration: If audio is less than this length, do not include in dataset
pad_to_max: if specified input sequences into dnn model will be padded to max_duration
blank_index: blank index for ctc loss / decoder
max_utts: Limit number of utterances
normalize: whether to normalize transcript text
sort_by_duration: whether or not to sort sequences by increasing duration
trim: if specified trims leading and trailing silence from an audio signal.
speed_perturbation: specify if using data contains speed perburbation
"""
m_paths = manifest_filepath.split(',')
self.manifest = Manifest(dataset_dir, m_paths, labels, blank_index, pad_to_max=pad_to_max,
max_duration=max_duration,
sort_by_duration=sort_by_duration,
min_duration=min_duration, max_utts=max_utts,
normalize=normalize, speed_perturbation=speed_perturbation)
self.featurizer = featurizer
self.blank_index = blank_index
self.trim = trim
print(
"Dataset loaded with {0:.2f} hours. Filtered {1:.2f} hours.".format(
self.manifest.duration / 3600,
self.manifest.filtered_duration / 3600))
def __init__(self, dataset_dir, manifest_filepath, labels,
sample_rate=16000, perf_count=None):
m_paths = [manifest_filepath]
self.manifest = Manifest(dataset_dir, m_paths, labels, len(labels),
normalize=True, max_duration=15.0)
self.sample_rate = sample_rate
self.count = len(self.manifest)
perf_count = self.count if perf_count is None else perf_count
self.sample_id_to_sample = {}
self.qsl = lg.ConstructQSL(self.count, perf_count,
self.load_query_samples,
self.unload_query_samples)
print(
"Dataset loaded with {0:.2f} hours. Filtered {1:.2f} hours. Number of samples: {2}".format(
self.manifest.duration / 3600,
self.manifest.filtered_duration / 3600,
self.count))
def main():
args = get_args()
labels = [" ", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z", "'"]
manifest = Manifest(args.dataset_dir, [args.manifest], labels, len(labels), normalize=True, max_duration=15.0)
with open(os.path.join(args.log_dir, "mlperf_log_accuracy.json")) as fh:
results = json.load(fh)
hypotheses = []
references = []
for result in results:
hypotheses.append(array.array(dtype_map[args.output_dtype], bytes.fromhex(result["data"])).tolist())
references.append(manifest[result["qsl_idx"]]["transcript"])
references = __gather_predictions([references], labels=labels)
hypotheses = __gather_predictions([hypotheses], labels=labels)
d = dict(predictions=hypotheses,
transcripts=references)
wer = process_evaluation_epoch(d)
print("Word Error Rate: {:}%, accuracy={:}%".format(wer * 100, (1 - wer) * 100))