Datasets:

Codyfederer
/

test3

Error code:   StreamingRowsError
Exception:    RuntimeError
Message:      Could not open input file: segments/segment_000000_speaker_0.wav No such file or directory
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2361, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1883, in __iter__
                  batch = formatter.format_batch(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 471, in format_batch
                  batch = self.python_features_decoder.decode_batch(batch)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 233, in decode_batch
                  return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2142, in decode_batch
                  [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2143, in <listcomp>
                  decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1405, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 182, in decode_example
                  audio = AudioDecoder(path, stream_index=self.stream_index, sample_rate=self.sampling_rate)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/torchcodec/decoders/_audio_decoder.py", line 61, in __init__
                  self._decoder = create_decoder(source=source, seek_mode="approximate")
                File "/src/services/worker/.venv/lib/python3.9/site-packages/torchcodec/decoders/_decoder_utils.py", line 27, in create_decoder
                  return core.create_from_file(source, seek_mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/torch/_ops.py", line 756, in __call__
                  return self._op(*args, **kwargs)
              RuntimeError: Could not open input file: segments/segment_000000_speaker_0.wav No such file or directory

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test3

This is a merged speech dataset containing 345 audio segments from 2 source datasets.

Dataset Information

Total Segments: 345
Speakers: 7
Languages: en
Emotions: happy, neutral, angry, sad
Original Datasets: 2

Dataset Structure

Each example contains:

audio: Audio file (WAV format, 16kHz sampling rate)
text: Transcription of the audio
speaker_id: Unique speaker identifier (made unique across all merged datasets)
emotion: Detected emotion (neutral, happy, sad, etc.)
language: Language code (en, es, fr, etc.)

Usage

Loading the Dataset

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Codyfederer/test3")

# Access the training split
train_data = dataset["train"]

# Example: Get first sample
sample = train_data[0]
print(f"Text: {sample['text']}")
print(f"Speaker: {sample['speaker_id']}")
print(f"Language: {sample['language']}")
print(f"Emotion: {sample['emotion']}")

# Play audio (requires audio libraries)
# sample['audio']['array'] contains the audio data
# sample['audio']['sampling_rate'] contains the sampling rate

Alternative: Load from CSV

import pandas as pd
from datasets import Dataset, Audio, Features, Value

# Load the CSV file
df = pd.read_csv("data.csv")

# Define features
features = Features({
    "audio": Audio(sampling_rate=16000),
    "text": Value("string"),
    "speaker_id": Value("string"),
    "emotion": Value("string"),
    "language": Value("string")
})

# Create dataset
dataset = Dataset.from_pandas(df, features=features)

Dataset Structure

The dataset includes:

data.csv - Main dataset file with all columns
segments/ - Directory containing all audio files
load_dataset.txt - Python script for loading the dataset (rename to .py to use)

CSV columns:

audio: Path to the audio file (in segments/ directory)
text: Transcription of the audio
speaker_id: Unique speaker identifier
emotion: Detected emotion
language: Language code

Speaker ID Mapping

Speaker IDs have been made unique across all merged datasets to avoid conflicts. For example:

Original Dataset A: speaker_0, speaker_1
Original Dataset B: speaker_0, speaker_1
Merged Dataset: speaker_0, speaker_1, speaker_2, speaker_3

Original dataset information is preserved in the metadata for reference.

Data Quality

This dataset was created using the Vyvo Dataset Builder with:

Automatic transcription and diarization
Quality filtering for audio segments
Music and noise filtering
Emotion detection
Language identification

License

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Citation

@dataset{vyvo_merged_dataset,
  title={test3},
  author={Vyvo Dataset Builder},
  year={2025},
  url={https://huggingface.co/datasets/Codyfederer/test3}
}

This dataset was created using the Vyvo Dataset Builder tool.

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