Model reference · open weights

wav2vec2-large-xlsr-galician

Available as managed deployment Audio ifrz · community Speech→text 1 variants 286k dl/mo

wav2vec2-large-xlsr-galician is an open-weight audio or speech model from ifrz. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released byifrz
TypeAudio & music
TaskSpeech→text
Runs withtransformers
Released2022-04-29
Popularity286k downloads / month
LicenceUnknown

About

What wav2vec2-large-xlsr-galician is


language: gl datasets:

  • OpenSLR 77
  • mozilla-foundation common_voice_8_0 metrics:
  • wer tags:
  • audio
  • automatic-speech-recognition
  • speech
  • xlsr-fine-tuning-week license: apache-2.0 model-index:
  • name: Galician wav2vec2-large-xlsr-galician results:
    • task: name: Speech Recognition type: automatic-speech-recognition dataset_1: name: OpenSLR type: openslr args: gl dataset_2: name: mozilla-foundation type: common voice args: gl metrics:
      • name: Test WER type: wer value: 7.12

Read the full model card

Model

Fine-tuned model for Galician language

Based on the facebook/wav2vec2-large-xlsr-53 self-supervised model Fine-tune with audio labelled from OpenSLR and Mozilla Common_Voice (both datasets previously refined)

Check training metrics to see results

Testing

Make sure that the audio speech input is sampled at 16kHz (mono).

from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

model = Wav2Vec2ForCTC.from_pretrained("ifrz/wav2vec2-large-xlsr-galician")
processor = Wav2Vec2Processor.from_pretrained("ifrz/wav2vec2-large-xlsr-galician")

# Reading taken audio clip
import librosa, torch
audio, rate = librosa.load("./gl_test_1.wav", sr = 16000)

# Taking an input value
input_values = processor(audio, sampling_rate=16_000, return_tensors = "pt", padding="longest").input_values
# Storing logits (non-normalized prediction values)
logits = model(input_values).logits
# Storing predicted ids
prediction = torch.argmax(logits, dim = -1)

# Passing the prediction to the tokenzer decode to get the transcription
transcription = processor.batch_decode(prediction)[0]
print(transcription)

From the published model card. Full card on the HuggingFace links in the sidebar.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys wav2vec2-large-xlsr-galician for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (wav2vec2-large-xlsr-galician below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="wav2vec2-large-xlsr-galician" -F file=@audio.mp3

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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