---
language:
- pt
license: cc-by-nc-sa-4.0
pretty_name: TAGARELA
task_categories:
- automatic-speech-recognition
- text-to-speech
tags:
- portuguese
- brazilian-portuguese
- european-portuguese
- pt-br
- pt-pt
- speech
- audio
- asr
- tts
- podcasts
- non-commercial
size_categories:
- 1M<n<10M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: audio
    dtype:
      audio:
        sampling_rate: 16000
  - name: path
    dtype: string
  - name: sentence
    dtype: string
  - name: accent
    dtype: string
  splits:
  - name: train
    num_bytes: 1763288239384
    num_examples: 7111196
  download_size: 1212594582487
  dataset_size: 1763288239384
---

# TAGARELA: A Portuguese Speech Dataset From Podcasts

TAGARELA is a large-scale Portuguese speech dataset built from podcast audio and curated for speech technology research, especially Automatic Speech Recognition (ASR) and Text-to-Speech (TTS).

The dataset contains more than **8,972 hours** of Portuguese speech derived from the *Cem Mil Podcasts* collection. It includes Brazilian Portuguese and European Portuguese speech, processed through a pipeline involving audio standardization, segmentation, speaker diarization, overlap detection, transcription generation, and quality enhancement.

## News

- **2026:** TAGARELA was published at **ICASSP 2026**.
- **2026:** TAGARELA was released publicly on Hugging Face for Portuguese speech research.

## Dataset Details

### Dataset Description

TAGARELA was created to reduce the resource gap for Portuguese speech technologies. While English has several large-scale speech corpora, Portuguese remains comparatively under-resourced, especially for large-scale ASR and TTS training.

The dataset provides audio-text pairs suitable for speech recognition and speech generation research. The full curated corpus contains:

- **8,972+ hours** of Portuguese speech
- **16,806 podcast episodes**
- **2,094 podcast shows**
- **13,368 distinct speakers**
- **8,130 hours** of Brazilian Portuguese (`pt-BR`)
- **842 hours** of European Portuguese (`pt-PT`)
- **6,368 hours** from male speakers
- **2,604 hours** from female speakers
- Audio segments with an average duration of approximately **9.30 ± 5.49 seconds**
- Transcriptions with an average length of approximately **27.69 ± 17.06 words**

TAGARELA includes a full subset intended for robust ASR training and a clean-speech subset of approximately **2,800 hours** intended for high-quality TTS and speech generation tasks.

### Dataset Sources

- **Project page:** https://fredso.com.br/TAGARELA
- **arXiv:** https://arxiv.org/abs/2603.15326
- **IEEE Xplore (ICASSP 2026):** https://ieeexplore.ieee.org/document/11462137

## Motivation

Portuguese is spoken by hundreds of millions of people but remains comparatively under-resourced in speech technology. Large-scale, public, and high-quality speech datasets are essential for training robust ASR and TTS systems. TAGARELA was created to support research and development of Portuguese speech technologies at a scale closer to major English speech corpora.

The dataset is intended to support:

- Portuguese ASR research
- Portuguese TTS research
- Speech generation research
- Robust modeling of podcast speech
- Research involving Brazilian Portuguese and European Portuguese
- Evaluation of speech models trained on large-scale Portuguese audio

## Dataset Usage

TAGARELA is distributed through Hugging Face Datasets in Parquet format.

Because the dataset is large, streaming is recommended for most use cases.

### Install Dependencies

```bash
pip install datasets soundfile librosa
```

If Hugging Face authentication is required in your environment, use:

```bash
huggingface-cli login
```

### Load with Streaming

Streaming avoids downloading the full dataset before use.

```python
from datasets import load_dataset

dataset = load_dataset(
    "freds0/TAGARELA",
    split="train",
    streaming=True
)

sample = next(iter(dataset))

print(sample.keys())
print(sample["sentence"])
print(sample["path"])
print(sample["audio"])
```

### Iterate Over Samples

```python
from datasets import load_dataset

dataset = load_dataset(
    "freds0/TAGARELA",
    split="train",
    streaming=True
)

for i, sample in enumerate(dataset):
    audio = sample["audio"]
    text = sample["sentence"]

    print(i)
    print("Text:", text)
    print("Sampling rate:", audio["sampling_rate"])
    print("Audio array shape:", audio["array"].shape)

    if i == 4:
        break
```

### Load Without Decoding Audio

If you only need file paths and transcriptions, you can avoid decoding audio during iteration.

```python
from datasets import load_dataset, Audio

dataset = load_dataset(
    "freds0/TAGARELA",
    split="train",
    streaming=True
)

dataset = dataset.cast_column("audio", Audio(decode=False))

sample = next(iter(dataset))

print(sample["path"])
print(sample["sentence"])
print(sample["audio"])
```

### Download the Dataset Locally

Downloading the full dataset requires substantial disk space. The current dataset metadata reports a download size of approximately **1.21 TB** and a dataset size of approximately **1.76 TB**.

```python
from datasets import load_dataset

dataset = load_dataset(
    "freds0/TAGARELA",
    split="train"
)

print(dataset)
```

### Use for ASR Fine-Tuning

A typical ASR fine-tuning pipeline can use the `audio` field as input and the `sentence` field as the target transcription.

```python
from datasets import load_dataset

dataset = load_dataset(
    "freds0/TAGARELA",
    split="train",
    streaming=True
)

for sample in dataset:
    audio_array = sample["audio"]["array"]
    sampling_rate = sample["audio"]["sampling_rate"]
    transcription = sample["sentence"]

    # Use audio_array and transcription in your ASR training pipeline.
    break
```

### Use for TTS Training

For TTS training, use the `sentence` field as text input and the corresponding `audio` field as the target speech signal.

```python
from datasets import load_dataset

dataset = load_dataset(
    "freds0/TAGARELA",
    split="train",
    streaming=True
)

for sample in dataset:
    text = sample["sentence"]
    audio = sample["audio"]

    # Use text and audio in your TTS training pipeline.
    break
```

## Intended Uses

### Direct Uses

TAGARELA can be used for:

- Training Portuguese ASR models
- Fine-tuning multilingual or Portuguese speech recognition models
- Training Portuguese TTS models
- Training speech generation systems
- Studying robust speech modeling from podcast audio
- Evaluating Portuguese speech models
- Research on Brazilian and European Portuguese speech
- Research on large-scale speech corpus construction
- Developing preprocessing, filtering, or quality estimation methods for podcast speech

### Out-of-Scope Uses

TAGARELA should not be used for:

- Speaker identification of private individuals
- Speaker verification or biometric recognition
- Surveillance or tracking
- Unauthorized voice cloning
- Impersonation or deceptive synthetic media generation
- Applications that violate privacy, copyright, publicity rights, or platform terms of the original podcast sources
- High-stakes decision-making without additional validation

Users are responsible for ensuring that their use of the dataset complies with applicable laws, ethical guidelines, institutional policies, and the terms associated with the original podcast sources.

## Dataset Structure

The dataset contains a single split:

```python
DatasetDict({
    train: Dataset({
        features: ["audio", "path", "sentence"],
        num_rows: 7111196
    })
})
```

### Data Fields

- `audio`: Audio sample loaded as a Hugging Face `Audio` feature, sampled at 16 kHz.
- `path`: Path or identifier for the corresponding audio file.
- `sentence`: Text transcription associated with the audio segment.

### Data Splits

| Split | Examples |
|---|---:|
| train | 7,111,196 |

### Released Fields and Corpus-Level Metadata

The default Hugging Face release contains the fields `audio`, `path`, and `sentence`.

Statistics about dialect, gender, and speakers describe the corpus-level distribution and may not be available as columns in the default dataset split.

## Dataset Creation

### Source Data

TAGARELA was derived from the *Cem Mil Podcasts* collection, a large Portuguese podcast corpus containing more than 76,000 hours of diverse, multi-dialect Portuguese audio. The original collection contains raw podcast audio and automatically generated transcripts.

TAGARELA applies additional processing to make the data more suitable for ASR and TTS model training.

### Dataset Subsets

| Subset | Main task | Size | Description |
|---|---:|---:|---|
| Full subset | ASR | 8,972 hours | Includes audio containing various types of disfluencies, designed for robust automatic speech recognition training. |
| Clean-speech subset | TTS | 2,800 hours | Curated speech-only subset designed for high-quality text-to-speech and speech generation tasks. |

### Processing Pipeline

TAGARELA was created through a multi-stage processing pipeline:

1. **Audio standardization**  
   Audio files were converted to FLAC format with 16 kHz sample rate, 16-bit depth, and mono channel.

2. **Segmentation**  
   Long-form recordings were segmented into 5-20 second clips at natural silence points to maintain speech cohesiveness.

3. **Speaker diarization**  
   The pyannote framework was applied to identify and label speech segments for each speaker individually.

4. **Overlapping speech detection**  
   A Wav2Vec2-XLS-R classifier was trained to identify and discard segments with overlapping speech.

5. **Transcription generation**  
   A bootstrap strategy was used with ElevenLabs Scribe for a seed corpus, followed by fine-tuning Whisper large-v3 for pseudo-labeling with quality filtering via Wav2Vec2-XLS-R agreement.

6. **Quality enhancement**  
   A Vocos vocoder was repurposed as a denoiser to remove background noise, hiss, and light reverberation.

### Annotations

The main annotation is the `sentence` transcription. These transcriptions are generated through a bootstrap ASR strategy and should be treated as large-scale ASR-generated labels rather than fully manual annotations.

## Benchmark Results

### Automatic Speech Recognition

Models trained on TAGARELA and evaluated on the Common Voice 17.0 Portuguese test set:

| Model | WER (%) ↓ |
|---|---:|
| Canary-1B-Flash | 7.8 |
| Distil-Whisper | 9.2 |
| Parakeet TDT | 12.3 |

### Text-to-Speech

Models trained on the 2,800-hour clean-speech subset:

| Model | CER (%) ↓ | WER (%) ↓ | MOS ↑ |
|---|---:|---:|---:|
| Orpheus-TTS | 19.32 ± 31.64 | 26.81 ± 35.57 | 4.00 ± 0.94 |
| Chatterbox | 23.73 ± 26.17 | 31.50 ± 30.05 | 4.53 ± 0.25 |

## Biases and Representational Considerations

TAGARELA reflects the characteristics of the podcast data from which it was derived. Users should consider the following representational factors:

- The corpus is strongly weighted toward Brazilian Portuguese, with approximately **91%** of the audio in `pt-BR` and **9%** in `pt-PT`.
- The corpus is imbalanced by speaker gender, with approximately **70%** of the audio from male speakers and **30%** from female speakers.
- Podcast content may overrepresent people, topics, speaking styles, production practices, and social groups that are more likely to publish podcasts.
- Speech style may include informal speech, spontaneous speech, disfluencies, interruptions, and domain-specific vocabulary.
- ASR-generated labels may introduce transcription biases or systematic errors from the models used in the transcription pipeline.

These factors should be considered when training, evaluating, or deploying models based on TAGARELA.

## Limitations

TAGARELA is a large-scale automatically processed speech dataset. Users should consider the following limitations:

- Transcriptions are generated through a large-scale ASR-based transcription pipeline and may contain errors.
- Podcast audio can contain background noise, informal speech, disfluencies, interruptions, and topic variability.
- Some remaining alignment errors may affect TTS training.
- Dialect, gender, speaker, and content distributions may reflect the biases of the source podcast collection.
- The dataset may contain sensitive, personal, copyrighted, or opinionated content from podcast speech.
- The dataset should be used with care when training generative speech models.

## Ethical Considerations

TAGARELA is based on podcast audio. Although the dataset is intended for research in speech technologies, users should consider privacy, consent, copyright, and potential misuse risks.

The dataset should not be used to build systems for speaker impersonation, unauthorized voice cloning, surveillance, biometric recognition, or deceptive synthetic media generation. Generated speech systems trained on this dataset should include safeguards against misuse.

### Consent and Privacy

The dataset is derived from podcast speech. Speakers may not have provided direct consent for all downstream machine learning uses.

Users should treat voice data as sensitive and avoid attempts to identify, profile, contact, track, clone, or impersonate speakers.

### Safety Considerations for Generative Models

When TAGARELA is used for speech generation or TTS research, users should include safeguards against misuse, including unauthorized voice cloning, impersonation, and deceptive synthetic media generation.

## Distribution

TAGARELA is distributed through the Hugging Face dataset repository in Parquet format.

Redistribution and derivative releases must comply with the dataset license, **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)**.

Users creating derivatives, filtered subsets, annotations, or adapted versions should preserve attribution and distribute derivative material under the same license when required by CC BY-NC-SA 4.0.

## Maintenance

The dataset is maintained through the TAGARELA Hugging Face repository and project page.

Users should check the project page and repository for updates, corrections, model releases, changes to documentation, or additional usage recommendations.

## License

TAGARELA is released under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)**.

This means users are free to share and adapt the material under the following terms:

- **Attribution:** Users must give appropriate credit, provide a link to the license, and indicate if changes were made.
- **NonCommercial:** Users may not use the material for commercial purposes.
- **ShareAlike:** If users remix, transform, or build upon the material, they must distribute their contributions under the same license.

License details: https://creativecommons.org/licenses/by-nc-sa/4.0/

## Citation

If you use TAGARELA, please cite:

```bibtex
@INPROCEEDINGS{11462137,
  author={De Oliveira, Frederico Santos and Gris, Lucas Rafael Stefanel and Ferreira, Alef Iury Siqueira and Da Rosa, Augusto Seben and Filho, Alexandre Costa Ferro and Casanova, Edresson and Shulby, Christopher Dane and Sousa, Rafael Teixeira and Silva, Diogo Fernandes Costa and Da Silva Soares, Anderson and Filho, Arlindo Rodrigues Galvão},
  booktitle={ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={TAGARELA - A Portuguese Speech Dataset from Podcasts}, 
  year={2026},
  volume={},
  number={},
  pages={15517-15521},
  keywords={Low earth orbit satellites;Digital audio broadcasting;Broadcasting;Filtering;Filters;Circuits and systems;Digital audio broadcasting;Internet of Things;Avatars;Communication systems;speech processing;text-to-speech;dataset;automatic-speech-recognition},
  doi={10.1109/ICASSP55912.2026.11462137}
}
```

## Authors

Frederico Santos de Oliveira¹, Lucas Rafael Stefanel Gris², Alef Iury Siqueira Ferreira², Augusto Seben da Rosa³, Alexandre Costa Ferro Filho², Edresson Casanova⁴, Christopher Dane Shulby⁵, Rafael Teixeira Sousa¹, Diogo Fernandes Costa Silva², Anderson da Silva Soares², Arlindo Rodrigues Galvão Filho²

¹ Federal University of Mato Grosso (UFMT)  
² Federal University of Goiás (UFG)  
³ Paulista State University (UNESP)  
⁴ NVIDIA  
⁵ Elsa Speak

## Acknowledgements

This work has been fully funded by the project Research and Development of Algorithms for Construction of Digital Human Technological Components supported by the Advanced Knowledge Center in Immersive Technologies (AKCIT) in partnership with the Federal University of Goiás (UFG).