NURC-SP_ENTOA_TTS.py

import csv
import datasets
from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
from pathlib import Path
import os

_PROMPTS_URLS = {
    "dev": "prosodic/validation.csv",
    "train": "prosodic/train.csv",
}

_PROMPTS_FILTERED_URLS = {
    "dev": "prosodic/validation.csv",
    "train": "prosodic/train.csv",
}

_ARCHIVES = {
    "dev": "prosodic.tar.gz",
    "train": "prosodic.tar.gz",
}

_PATH_TO_CLIPS = {
    "dev": "",
    "train": "",
}

class NurcSPConfig(BuilderConfig):
    def __init__(self, prompts_type="original", **kwargs):
        super().__init__(**kwargs)
        self.prompts_type = prompts_type

class NurcSPDataset(GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        NurcSPConfig(name="original", description="Original audio prompts", prompts_type="original"),
        NurcSPConfig(name="filtered", description="Filtered audio prompts", prompts_type="filtered"),
    ]

    def _info(self):
        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),
                }
            )
        )

    def _split_generators(self, dl_manager):
        print("\n=== Configuration ===")
        print(f"Using prompts_type: {self.config.prompts_type}")
        
        prompts_urls = _PROMPTS_URLS
        if self.config.prompts_type == "filtered":
            prompts_urls = _PROMPTS_FILTERED_URLS
        
        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: {_ARCHIVES}")
        archive = dl_manager.download(_ARCHIVES)
        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)
            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)
        
        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")