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import csv
import datasets
from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
_PROSODIC_PROMPTS_URLS = {
"validation": "prosodic/validation.csv",
"train": "prosodic/train.csv",
}
_AUTOMATIC_PROMPTS_URLS = {
"validation": "automatic/validation.csv",
"train": "automatic/train.csv",
}
_ARCHIVES = {
"prosodic": "prosodic/audios.tar.gz",
"automatic": "automatic/audios.tar.gz",
}
_PATH_TO_CLIPS = {
"validation_prosodic": "prosodic/audios",
"train_prosodic": "prosodic/audios",
"validation_automatic": "automatic/audios/validation",
"train_automatic": "automatic/audios/train",
}
class EntoaConfig(BuilderConfig):
def __init__(self, prompts_type="prosodic", **kwargs):
super().__init__(**kwargs)
self.prompts_type = prompts_type
class EntoaDataset(GeneratorBasedBuilder):
BUILDER_CONFIGS = [
EntoaConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"),
EntoaConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"),
]
def _info(self):
if self.config.name == "prosodic":
features = datasets.Features(
{
"path": datasets.Value("string"),
"name": datasets.Value("string"),
"speaker": datasets.Value("string"),
"start_time": datasets.Value("string"),
"end_time": datasets.Value("string"),
"normalized_text": datasets.Value("string"),
"text": datasets.Value("string"),
"duration": datasets.Value("string"),
"type": datasets.Value("string"),
"year": datasets.Value("string"),
"gender": datasets.Value("string"),
"age_range": datasets.Value("string"),
"total_duration": datasets.Value("string"),
"quality": datasets.Value("string"),
"theme": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
}
)
else: # automatic
features = datasets.Features(
{
"audio_name": datasets.Value("string"),
"file_path": datasets.Value("string"),
"text": datasets.Value("string"),
"start_time": datasets.Value("string"),
"end_time": datasets.Value("string"),
"duration": datasets.Value("string"),
"quality": datasets.Value("string"),
"speech_genre": datasets.Value("string"),
"speech_style": datasets.Value("string"),
"variety": datasets.Value("string"),
"accent": datasets.Value("string"),
"sex": datasets.Value("string"),
"age_range": datasets.Value("string"),
"num_speakers": datasets.Value("string"),
"speaker_id": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=16_000),
}
)
return DatasetInfo(features=features)
def _split_generators(self, dl_manager):
prompts_urls = _PROSODIC_PROMPTS_URLS if self.config.name == "prosodic" else _AUTOMATIC_PROMPTS_URLS
archive = dl_manager.download(_ARCHIVES[self.config.name])
prompts_path = dl_manager.download(prompts_urls)
# Debug prints for downloaded paths
print(f"Downloaded prompts: {prompts_path}")
print(f"Downloaded archive: {archive}")
return [
SplitGenerator(
name=Split.VALIDATION,
gen_kwargs={
"prompts_path": prompts_path["validation"],
"path_to_clips": _PATH_TO_CLIPS[f"validation_{self.config.name}"],
"audio_files": dl_manager.iter_archive(archive),
},
),
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={
"prompts_path": prompts_path["train"],
"path_to_clips": _PATH_TO_CLIPS[f"train_{self.config.name}"],
"audio_files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, prompts_path, path_to_clips, audio_files):
examples = {}
# Debug print for prompts path
print(f"Reading prompts from: {prompts_path}")
with open(prompts_path, "r") as f:
csv_reader = csv.DictReader(f)
for row in csv_reader:
# Debug print for row processing
print(f"Processing row: {row}")
if self.config.name == "prosodic":
examples[row['path']] = {
"path": row['path'],
"name": row['name'],
"speaker": row['speaker'],
"start_time": row['start_time'],
"end_time": row['end_time'],
"normalized_text": row['normalized_text'],
"text": row['text'],
"duration": row['duration'],
"type": row['type'],
"year": row['year'],
"gender": row['gender'],
"age_range": row['age_range'],
"total_duration": row['total_duration'],
"quality": row['quality'],
"theme": row['theme'],
}
else: # automatic
examples[row['file_path']] = {
"audio_name": row['audio_name'],
"file_path": row['file_path'],
"text": row['text'],
"start_time": row['start_time'],
"end_time": row['end_time'],
"duration": row['duration'],
"quality": row['quality'],
"speech_genre": row['speech_genre'],
"speech_style": row['speech_style'],
"variety": row['variety'],
"accent": row['accent'],
"sex": row['sex'],
"age_range": row['age_range'],
"num_speakers": row['num_speakers'],
"speaker_id": row['speaker_id'],
}
id_ = 0
inside_clips_dir = False
for path, f in audio_files:
if path.startswith(path_to_clips):
inside_clips_dir = True
# Debug print for matching audio file
print(f"Found matching audio file: {path}")
if path in examples:
audio = {"path": path, "bytes": f.read()}
yield id_, {**examples[path], "audio": audio}
id_ += 1
elif inside_clips_dir:
break
# Debug print for completion
print(f"Completed generating examples. Total examples: {id_}")
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