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Choosing a dataset

As with any machine learning problem, our model is only as good as the data that we train it on. Speech recognition datasets vary considerably in how they are curated and the domains that they cover. To pick the right dataset, we need to match our criteria with the features that a dataset offers.

Before we pick a dataset, we first need to understand the key defining features.

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Features of speech datasets

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1. Number of hours

Simply put, the number of training hours indicates how large the dataset is. It’s analogous to the number of training examples in an NLP dataset. However, bigger datasets aren’t necessarily better. If we want a model that generalises well, we want a diverse dataset with lots of different speakers, domains and speaking styles.

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2. Domain

The domain entails where the data was sourced from, whether it be audiobooks, podcasts, YouTube or financial meetings. Each domain has a different distribution of data. For example, audiobooks are recorded in high-quality studio conditions (with no background noise) and text that is taken from written literature. Whereas for YouTube, the audio likely contains more background noise and a more informal style of speech.

We need to match our domain to the conditions we anticipate at inference time. For instance, if we train our model on audiobooks, we can’t expect it to perform well in noisy environments.

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3. Speaking style

The speaking style falls into one of two categories:

- Narrated: read from a script
- Spontaneous: un-scripted, conversational speech

The audio and text data reflect the style of speaking. Since narrated text is scripted, it tends to be spoken articulately and without any errors:

    “Consider the task of training a model on a speech recognition dataset”

Whereas for spontaneous speech, we can expect a more colloquial style of speech, with the inclusion of repetitions, hesitations and false-starts:

    “Let’s uhh let's take a look at how you'd go about training a model on uhm a sp- speech recognition dataset”

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4. Transcription style

The transcription style refers to whether the target text has punctuation, casing or both. If we want a system to generate fully formatted text that could be used for a publication or meeting transcription, we require training data with punctuation and casing. If we just require the spoken words in an un-formatted structure, neither punctuation nor casing are necessary. In this case, we can either pick a dataset without punctuation or casing, or pick one that has punctuation and casing and then subsequently remove them from the target text through pre-processing.

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A summary of datasets on the Hub

Here is a summary of the most popular English speech recognition datasets on the Hugging Face Hub:

Dataset | Train Hours | Domain | Speaking Style | Casing | Punctuation | License | Recommended Use
--- | --- | --- | --- | --- | --- | --- | ---
LibriSpeech | 960 | Audiobook | Narrated | ❌ | ❌ | CC-BY-4.0 | Academic benchmarks
Common Voice 17 | 3000 | Wikipedia | Narrated | ✅ | ✅ | CC0-1.0 | Non-native speakers
VoxPopuli | 540 | European Parliament | Oratory | ❌ | ✅ | CC0 | Non-native speakers
TED-LIUM | 450 | TED talks | Oratory | ❌ | ❌ | CC-BY-NC-ND 3.0 | Technical topics
GigaSpeech | 10000 | Audiobook, podcast, YouTube | Narrated, spontaneous | ❌ | ✅ | apache-2.0 | Robustness over multiple domains
SPGISpeech | 5000 | Financial meetings | Oratory, spontaneous | ✅ | ✅ | User Agreement | Fully formatted transcriptions
Earnings-22 | 119 | Financial meetings | Oratory, spontaneous | ✅ | ✅ | CC-BY-SA-4.0 | Diversity of accents
AMI | 100 | Meetings | Spontaneous | ✅ | ✅ | CC-BY-4.0 | Noisy speech conditions

This table serves as a reference for selecting a dataset based on your criterion. Below is an equivalent table for multilingual speech recognition. Note that we omit the train hours column, since this varies depending on the language for each dataset, and replace it with the number of languages per dataset:

Dataset | Languages | Domain | Speaking Style | Casing | Punctuation | License | Recommended Usage
--- | --- | --- | --- | --- | --- | --- | ---
Multilingual LibriSpeech | 6 | Audiobooks | Narrated | ❌ | ❌ | CC-BY-4.0 | Academic benchmarks
Common Voice 17 | 124 | Wikipedia text & crowd-sourced speech | Narrated | ✅ | ✅ | CC0-1.0 | Diverse speaker set
VoxPopuli | 15 | European Parliament recordings | Spontaneous | ❌ | ✅ | CC0 | European languages
FLEURS | 101 | European Parliament recordings | Spontaneous | ❌ | ❌ | CC-BY-4.0 | Multilingual evaluation

For a detailed breakdown of the audio datasets covered in both tables, refer to the blog post A Complete Guide to Audio Datasets. While there are over 180 speech recognition datasets on the Hub, it may be possible that there isn’t a dataset that matches your needs. In this case, it’s also possible to use your own audio data with 🤗 Datasets. To create a custom audio dataset, refer to the guide Create an audio dataset. When creating a custom audio dataset, consider sharing the final dataset on the Hub so that others in the community can benefit from your efforts - the audio community is inclusive and wide-ranging, and others will appreciate your work as you do theirs.

Alright! Now that we’ve gone through all the criterion for selecting an ASR dataset, let’s pick one for the purpose of this tutorial. We know that Whisper already does a pretty good job at transcribing data in high-resource languages (such as English and Spanish), so we’ll focus ourselves on low-resource multilingual transcription. We want to retain Whisper’s ability to predict punctuation and casing, so it seems from the second table that Common Voice 17 is a great candidate dataset!

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Common Voice 17

Common Voice 17 is a crowd-sourced dataset where speakers record text from Wikipedia in various languages. It forms part of the Common Voice series, a collection of Common Voice datasets released by Mozilla Foundation. At the time of writing, Common Voice 17 is the latest edition of the dataset, with the most languages and hours per language out of any release to date.

We can get the full list of languages for the Common Voice 17 dataset by checking-out the dataset page on the Hub: mozilla-foundation/common_voice_17_0. The first time you view this page, you’ll be asked to accept the terms of use. After that, you’ll be given full access to the dataset.

Once we’ve provided authentication to use the dataset, we’ll be presented with the dataset preview. The dataset preview shows us the first 100 samples of the dataset for each language. What’s more, it’s loaded up with audio samples ready for us to listen to in real time. For this Unit, we’ll select Dhivehi (or Maldivian), an Indo-Aryan language spoken in the South Asian island country of the Maldives. While we’re selecting Dhivehi for this tutorial, the steps covered here apply to any one of the 124 languages in the Common Voice 17 dataset, and more generally to any one of the 180+ audio datasets on the Hugging Face Hub, so there’s no restriction on language or dialect.

We can select the Dhivehi subset of Common Voice 17 by setting the subset to `dv` using the dropdown menu (`dv` being the language identifier code for Dhivehi):

If we hit the play button on the first sample, we can listen to the audio and see the corresponding text. Have a scroll through the samples for the train and test sets to get a better feel for the audio and text data that we’re dealing with. You can tell from the intonation and style that the recordings are taken from narrated speech. You’ll also likely notice the large variation in speakers and recording quality, a common trait of crowd-sourced data.

The Dataset Preview is a brilliant way of experiencing audio datasets before committing to using them. You can pick any dataset on the Hub, scroll through the samples and listen to the audio for the different subsets and splits, gauging whether it’s the right dataset for your needs. Once you’ve selected a dataset, it’s trivial to load the data so that you can start using it.

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Model-Specific Dataset Considerations

When selecting a dataset for fine-tuning, it’s important to consider the specific requirements and characteristics of your chosen ASR model:

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For Whisper Models

- Multilingual datasets work best due to Whisper’s multilingual training
- Longer audio segments (up to 30 seconds) are processed efficiently
- Diverse domains benefit from Whisper’s robust pre-training
- Punctuation and casing are recommended as Whisper handles them well

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For Moonshine Models

- English-only datasets are required (currently)
- Shorter audio segments (under 10 seconds) leverage Moonshine’s efficiency advantages
- Clean, high-quality audio maximizes the model’s edge computing benefits
- Domain-specific data can be particularly effective due to the model’s focused training

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For Kyutai STT Models

- English and French datasets are supported
- Streaming-compatible data with natural speech patterns work best
- Longer audio files (up to 2 hours) are handled efficiently
- Noisy or challenging audio conditions are well-supported

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Dataset Selection Strategy

1. Match your model’s language support to your target languages
2. Consider audio length characteristics for optimal performance
3. Evaluate computational constraints when choosing between models
4. Test on representative samples from your target domain

The Common Voice 17 dataset we selected works well for all three model types, though you might see different performance characteristics depending on your choice of model architecture.

For production deployments, you might also consider alternative ASR implementations that offer different optimization trade-offs, such as whisper.cpp for edge devices, faster-whisper for server applications, or MLX-Whisper for Apple Silicon devices.

Now, I personally don’t speak Dhivehi, and expect the vast majority of readers not to either! To know if our fine-tuned model is any good, we’ll need a rigorous way of evaluating it on unseen data and measuring its transcription accuracy. We’ll cover exactly this in the next section!

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