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import csv
import datasets
from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
from pathlib import Path
import os
_PROMPTS_PROSODIC_URLS = {
"dev": "prosodic/validation.csv",
"train": "prosodic/train.csv",
}
_PROMPTS_AUTOMATIC_URLS = {
"dev": "automatic/validation.csv",
"train": "automatic/train.csv",
}
_ARCHIVES_PROSODIC = {
"dev": "prosodic/audios.tar.gz",
"train": "prosodic/audios.tar.gz",
}
_ARCHIVES_AUTOMATIC = {
"dev": "automatic/audios.tar.gz",
"train": "automatic/audios.tar.gz",
}
_PATH_TO_CLIPS = {
"dev": "",
"train": "",
}
class NurcSPConfig(BuilderConfig):
def __init__(self, prompts_type, **kwargs):
super().__init__(**kwargs)
self.prompts_type = prompts_type
class NurcSPDataset(GeneratorBasedBuilder):
BUILDER_CONFIGS = [
NurcSPConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"),
NurcSPConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"),
]
def _info(self):
if self.config.name == "prosodic":
return DatasetInfo(
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),
}
)
)
elif self.config.name == "automatic":
return DatasetInfo(
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),
}
)
)
def _split_generators(self, dl_manager):
print("\n=== Configuration ===")
print(f"Using prompts_type: {self.config.prompts_type}")
if self.config.prompts_type == "prosodic":
prompts_urls = _PROMPTS_PROSODIC_URLS
archive_link = _ARCHIVES_PROSODIC
elif self.config.prompts_type == "automatic":
prompts_urls = _PROMPTS_AUTOMATIC_URLS
archive_link = _ARCHIVES_AUTOMATIC
else:
print("Invalid config")
return
print(f"Downloading prompts from: {prompts_urls}")
prompts_path = dl_manager.download(prompts_urls)
print(f"Downloaded prompts to: {prompts_path}")
print(f"Downloading archives from: {archive_link}")
archive = dl_manager.download(archive_link)
print(f"Downloaded archives to: {archive}")
return [
SplitGenerator(
name=Split.VALIDATION,
gen_kwargs={
"prompts_path": prompts_path["dev"],
"path_to_clips": _PATH_TO_CLIPS["dev"],
"audio_files": dl_manager.iter_archive(archive["dev"]),
"split_name": "validation"
}
),
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={
"prompts_path": prompts_path["train"],
"path_to_clips": _PATH_TO_CLIPS["train"],
"audio_files": dl_manager.iter_archive(archive["train"]),
"split_name": "train"
}
),
]
def _generate_examples(self, prompts_path, path_to_clips, audio_files, split_name):
print(f"\n{'='*50}")
print(f"Processing {split_name} split")
print(f"{'='*50}")
print(f"\nCSV Path: {prompts_path}")
print(f"Expected clips directory: {path_to_clips}")
examples = {}
csv_paths = []
# Read CSV file
print("\n=== Reading CSV ===")
with open(prompts_path, "r") as f:
csv_reader = csv.DictReader(f)
if self.config.prompts_type == "prosodic":
for row in csv_reader:
file_path = Path(row['path']).as_posix()
examples[file_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'],
}
csv_paths.append(file_path)
elif self.config.prompts_type == "automatic":
for row in csv_reader:
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'],
}
print(f"\nFound {len(csv_paths)} entries in CSV")
print("\nFirst 3 CSV paths:")
for path in csv_paths[:3]:
print(f" CSV path: {path}")
# Process archive
print("\n=== Processing Archive ===")
inside_clips_dir = False
id_ = 0
matched_files = 0
archive_paths = []
for path, f in audio_files:
path = Path(path).as_posix()
archive_paths.append(path)
if path.startswith(path_to_clips):
inside_clips_dir = True
if path in examples:
audio = {"path": path, "bytes": f.read()}
matched_files += 1
yield id_, {**examples[path], "audio": audio}
id_ += 1
print("\n=== Path Analysis ===")
print("\nFirst 3 archive paths:")
for path in archive_paths[:3]:
print(f" Archive path: {path}")
# Try to find potential matches
print("\nPotential matches in CSV:")
for csv_path in csv_paths[:3]:
print(f"\nComparing:")
print(f" Archive: {path}")
print(f" CSV: {csv_path}")
print(f" Archive parts: {path.split('/')}")
print(f" CSV parts: {csv_path.split('/')}")
print(f"\n=== Summary for {split_name} split ===")
print(f"Total paths in CSV: {len(csv_paths)}")
print(f"Total paths found in archive: {len(archive_paths)}")
print(f"Successfully matched files: {matched_files}")
if matched_files == 0:
print("\n!!! MATCHING FAILED !!!")
print("No files were matched between CSV and archive")
print("\nTroubleshooting:")
print("1. Check if CSV paths start with the clip directory name")
print("2. Check for case sensitivity issues")
print("3. Check for extra/missing directory levels")
print("4. Check path separator consistency") |