---

# wav2vec2-base-it-latin

This model is a fine-tuned version of [wav2vec2-base-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-base-it-voxpopuli)

The dataset used is the [poetaexmachina-mp3-recitations](https://github.com/lsb/poetaexmachina-mp3-recitations),
all of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's [database](https://github.com/lsb/poetaexmachina/blob/master/merged-scansions.db) of words with scansions).

It achieves the following [results](https://github.com/lsb/tironiculum/blame/trunk/wav2vec2%20base%20it%20latin.ipynb#L1234) on the evaluation set:

- Loss: 0.1943
- WER: 0.398

{"language": ["la"], "license": "agpl-3.0", "tags": ["robust-speech-event", "hf-asr-leaderboard"], "datasets": ["lsb/poetaexmachina-mp3-recitations"], "metrics": ["wer"], "model-index": [{"name": "wav2vec2-base-it-latin", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Speech Recognition"}, "dataset": {"name": "Poeta Ex Machina mp3 recitations", "type": "lsb/poetaexmachina-mp3-recitations"}, "metrics": [{"type": "wer", "value": 0.398, "name": "Test WER"}]}]}]}
transformers pytorch wav2vec2 automatic-speech-recognition robust-speech-event hf-asr-leaderboard la dataset:lsb/poetaexmachina-mp3-recitations license:agpl-3.0 model-index endpoints_compatible region:us
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #la #dataset-lsb/poetaexmachina-mp3-recitations #license-agpl-3.0 #model-index #endpoints_compatible #region-us 

---

# wav2vec2-base-it-latin

This model is a fine-tuned version of wav2vec2-base-it-voxpopuli

The dataset used is the poetaexmachina-mp3-recitations,
all of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's database of words with scansions).

It achieves the following results on the evaluation set:

- Loss: 0.1943
- WER: 0.398

# wav2vec2-base-it-latin

This model is a fine-tuned version of wav2vec2-base-it-voxpopuli

The dataset used is the poetaexmachina-mp3-recitations,
all of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's database of words with scansions).

It achieves the following results on the evaluation set:

- Loss: 0.1943
- WER: 0.398
TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #la #dataset-lsb/poetaexmachina-mp3-recitations #license-agpl-3.0 #model-index #endpoints_compatible #region-us 
 # wav2vec2-base-it-latin

This model is a fine-tuned version of wav2vec2-base-it-voxpopuli

The dataset used is the poetaexmachina-mp3-recitations,
all of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's database of words with scansions).

It achieves the following results on the evaluation set:

- Loss: 0.1943
- WER: 0.398
passage: TAGS
#transformers #pytorch #wav2vec2 #automatic-speech-recognition #robust-speech-event #hf-asr-leaderboard #la #dataset-lsb/poetaexmachina-mp3-recitations #license-agpl-3.0 #model-index #endpoints_compatible #region-us 
# wav2vec2-base-it-latin

This model is a fine-tuned version of wav2vec2-base-it-voxpopuli

The dataset used is the poetaexmachina-mp3-recitations,
all of the 2-series texts (vergil) and every tenth 1-series text (words from Poeta Ex Machina's database of words with scansions).

It achieves the following results on the evaluation set:

- Loss: 0.1943
- WER: 0.398