Universal Representations of Speech](https://arxiv.org/abs/2205.12446) Fleurs is the speech version of the FLoRes machine translation benchmark. We use 2009 n-way parallel sentences from the FLoRes dev and devtest publicly available sets, in 102 languages. Training sets have around 10 hours of supervision. Speakers of the train sets are different than speakers from the dev/test sets. Multilingual fine-tuning is used and ”unit error rate” (characters, signs) of all languages is averaged. Languages and results are also grouped into seven geographical areas: The datasets library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared…
SAVRN Model Hub · Datasets by Task
Speech Recognition Datasets
9 open-weight speech recognition datasets in the SAVRN Model Hub, with Shrikant Nayak, Shreesha and Kapture CX publishing the most.
9Datasets
4Publishers
2Licenses
Most Downloaded
| Dataset | Publisher | License | Monthly downloads |
|---|---|---|---|
| fleurs | cc-by-4.0 | 102.1k | |
| bolAIndia | Kapture CX | Not stated | 7.1k |
| Dhravani | Shreesha | cc-by-4.0 | 1.2k |
| dhravani-mit-test | Shrikant Nayak | cc-by-4.0 | 78 |
| dhravani-iitpatna-test | Shrikant Nayak | cc-by-4.0 | 77 |
| dhravani-IIT_Guwahati-test | Shrikant Nayak | cc-by-4.0 | 70 |
| dhravani-IGDTUW_Delhi-test | Shrikant Nayak | cc-by-4.0 | 65 |
| dhravani-iitdelhi-test | Shrikant Nayak | cc-by-4.0 | 62 |
| dhravani-iiitdelhi-test | Shrikant Nayak | cc-by-4.0 | 56 |
Licenses
| License | Datasets | Commercial use |
|---|---|---|
| cc-by-4.0 | 8 | Yes |
| not stated | 1 | Not stated |
Who Publishes Them
| Publisher | Datasets |
|---|---|
| Shrikant Nayak | 6 |
| Shreesha | 1 |
| Kapture CX | 1 |
| 1 |
All 9 Datasets
Human-side speech from production call recordings, cut into utterance-level chunks by a two-engine VAD (Silero + TEN) and transcribed by third-party ASR providers. Each row keeps the transcript, the provider's confidence, and full provenance back to the source recording. One config per transcription system, so their output stays separable. The combined config interleaves several transcription systems within each shard, so this is the breakdown across the whole dataset. The systems differ substantially, so treat them as separate sources when training. Each row carries the system that produced it in its provider and model columns; the labels below are withheld aliases for the same systems, in…
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference A web-based interface for preparing audio datasets to fine-tune OpenAI's Whisper model. This tool helps in recording, managing, and organizing voice recordings with their corresponding transcriptions, with support for cloud storage and authentication. - ⌨ Keyboard shortcuts for efficiency 1. Create a transcript CSV file with your content: 2. Start the Flask application: 3. Access the interface: 1. Authentication 2. Session Setup - Click "Start Session" 3. Recording - Use on-screen controls or keyboard shortcuts: - R: Start recording / Stop recording - Space: Play recording - Enter: Save…