

# Whisper-medium-et

This is a Whisper-medium model [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) finetuned on around 800 hours of diverse Estonian data.

## Model description
This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech.


## Intended uses & limitations

This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc.

## How to use

Use as any other Whisper model via HF transformers, or use a faster decoder like [faster-whisper](https://github.com/guillaumekln/faster-whisper).


#### Limitations and bias

Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:
  * Speech containing technical and other domain-specific terms
  * Children's speech
  * Non-native speech
  * Speech recorded under very noisy conditions or with a microphone far from the speaker
  * Very spontaneous and overlapping speech

## Training data
Acoustic training data:

| Type                  | Amount (h) |
|-----------------------|:------:|
| Broadcast speech      |   591  |
| Spontaneous speech    |   53   |
| Elderly speech corpus |   53   |
| Talks, lectures       |   49   |
| Parliament speeches   |   31   |
| *Total*               |   *761*  |



## Training procedure

Finetuned using Espnet, and then comverted to transformers format using [this](https://gist.github.com/alumae/2dcf473b667cec9d513b80ea24e94672) script. 
Finetuning procedure is similar to [this](https://huggingface.co/espnet/shihlun_asr_whisper_medium_finetuned_librispeech100) model.

## Evaluation results

### WER

WER results below are obtained using greedy decoding (i.e., beam size 1).

|Dataset | WER |
|---|---|
| Common Voice 8.0 | 13.8 |
| Common Voice 11.0 | 14.7 |

{"language": "et", "license": "cc-by-4.0", "tags": ["audio", "automatic-speech-recognition", "hf-asr-leaderboard"], "model-index": [{"name": "TalTechNLP/whisper-medium-et", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11", "type": "mozilla-foundation/common_voice_11_0", "config": "et", "split": "test"}, "metrics": [{"type": "wer", "value": 14.66, "name": "Test WER"}, {"type": "cer", "value": 3.76, "name": "Test CER"}]}, {"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 8", "type": "mozilla-foundation/common_voice_8_0", "config": "et", "split": "test"}, "metrics": [{"type": "wer", "value": 13.793, "name": "Test WER"}, {"type": "cer", "value": 3.194, "name": "Test CER"}]}]}]}
transformers pytorch whisper automatic-speech-recognition audio hf-asr-leaderboard et license:cc-by-4.0 model-index endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #et #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 

Whisper-medium-et
=================


This is a Whisper-medium model openai/whisper-medium finetuned on around 800 hours of diverse Estonian data.


Model description
-----------------


This is a general-purpose Estonian ASR model trained in the Lab of Language Technology at TalTech.


Intended uses & limitations
---------------------------


This model is intended for general-purpose speech recognition, such as broadcast conversations, interviews, talks, etc.


How to use
----------


Use as any other Whisper model via HF transformers, or use a faster decoder like faster-whisper.


#### Limitations and bias


Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:


* Speech containing technical and other domain-specific terms
* Children's speech
* Non-native speech
* Speech recorded under very noisy conditions or with a microphone far from the speaker
* Very spontaneous and overlapping speech


Training data
-------------


Acoustic training data:



Training procedure
------------------


Finetuned using Espnet, and then comverted to transformers format using this script.
Finetuning procedure is similar to this model.


Evaluation results
------------------


### WER


WER results below are obtained using greedy decoding (i.e., beam size 1).




#### Limitations and bias


Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:


* Speech containing technical and other domain-specific terms
* Children's speech
* Non-native speech
* Speech recorded under very noisy conditions or with a microphone far from the speaker
* Very spontaneous and overlapping speech


Training data
-------------


Acoustic training data:



Training procedure
------------------


Finetuned using Espnet, and then comverted to transformers format using this script.
Finetuning procedure is similar to this model.


Evaluation results
------------------ ### WER


WER results below are obtained using greedy decoding (i.e., beam size 1).
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #et #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
 #### Limitations and bias


Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:


* Speech containing technical and other domain-specific terms
* Children's speech
* Non-native speech
* Speech recorded under very noisy conditions or with a microphone far from the speaker
* Very spontaneous and overlapping speech


Training data
-------------


Acoustic training data:



Training procedure
------------------


Finetuned using Espnet, and then comverted to transformers format using this script.
Finetuning procedure is similar to this model.


Evaluation results
------------------ ### WER


WER results below are obtained using greedy decoding (i.e., beam size 1).
TAGS
#transformers #pytorch #whisper #automatic-speech-recognition #audio #hf-asr-leaderboard #et #license-cc-by-4.0 #model-index #endpoints_compatible #has_space #region-us 
#### Limitations and bias


Since this model was trained on mostly broadcast speech and texts from the web, it might have problems correctly decoding the following:


* Speech containing technical and other domain-specific terms
* Children's speech
* Non-native speech
* Speech recorded under very noisy conditions or with a microphone far from the speaker
* Very spontaneous and overlapping speech


Training data
-------------


Acoustic training data:



Training procedure
------------------


Finetuned using Espnet, and then comverted to transformers format using this script.
Finetuning procedure is similar to this model.


Evaluation results
------------------### WER


WER results below are obtained using greedy decoding (i.e., beam size 1).