Practical overview
When it fits
Use Huggingsound when you want a Python toolkit for working with Hugging Face CTC speech-recognition models, from transcription through evaluation and fine-tuning.
Who it is for
- Python developers building speech-to-text experiments
- Researchers comparing speech-recognition output against reference transcripts
- Teams fine-tuning a speech-recognition model with paired audio and text
Documented uses
- Transcribe audio using a CTC model hosted on Hugging Face Hub
- Retrieve transcription timestamps and character-level probabilities
- Evaluate recognition output with WER and CER
- Fine-tune a model using audio paths and reference transcripts
- Apply language-model-assisted decoding with supported decoders
Requirements & limits
The repository identifies the project as open source under the MIT license. Its README requires Python 3.8 or later and documents installation with pip.
- Recognition uses CTC models hosted on Hugging Face Hub.
- Transcription output includes character-level probabilities and start and end timestamps in milliseconds.
- The evaluation example returns WER and CER.
- Language-model-assisted decoding requires KenLM-format language-model files; the Kensho decoder example also requires ctcdecode.
- The README warns that its usage documentation is incomplete and points to repository examples and source code.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Transcribe audio files with a Hugging Face CTC model
Use a CTC model to produce transcripts and per-character timing and probability data for audio files.
Before you start
- Python 3.8 or later
- Huggingsound installed with pip
- A CTC model hosted on the Hugging Face Hub
- Audio file paths
Steps
Install the library with the command below.
pip install huggingsoundImport
SpeechRecognitionModeland initialize it with a Hugging Face model identifier.Pass audio file paths to
model.transcribe(audio_paths).Read the returned transcription, start and end timestamps, and character probabilities.
Expected outcome
A list of results containing a transcription and character-level timestamps and probabilities for each audio file.
Guide limitations
- The README says its usage documentation is incomplete.
- For MP3 loading trouble, the README suggests installing ffmpeg.
Evaluate recognition output against reference transcripts
Calculate word and character error rates for audio paired with reference transcriptions.
Before you start
- Python 3.8 or later
- Huggingsound installed with pip
- A Hugging Face CTC model
- Audio paths paired with reference transcription text
Steps
Initialize
SpeechRecognitionModelwith a model identifier.Create a list of reference records, each containing an audio
pathandtranscription.Pass the records to
model.evaluate(references).Inspect the returned WER and CER values.
Expected outcome
An evaluation result with word error rate (`wer`) and character error rate (`cer`).
Guide limitations
- The README describes its usage documentation as incomplete.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- jonatasgrosman/huggingsound · Official homepage title · retained in its source language