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wav2vec2-large-xlsr-53-spanish-ep5-944h

Automatic Speech Recognition
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PyTorch
Safetensors
Spanish
wav2vec2
audio
spanish
xlrs-53-spanish
ciempiess
cimpiess-unam
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metadata
language: es
datasets:
  - ciempiess/ciempiess_light
  - ciempiess/ciempiess_balance
  - ciempiess/ciempiess_fem
  - common_voice
  - hub4ne_es_LDC98S74
  - callhome_es_LDC96S35
tags:
  - audio
  - automatic-speech-recognition
  - spanish
  - xlrs-53-spanish
  - ciempiess
  - cimpiess-unam
license: cc-by-4.0
widget: null
model-index:
  - name: wav2vec2-large-xlsr-53-spanish-ep5-944h
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Mozilla Common Voice 10.0 (Test)
          type: mozilla-foundation/common_voice_10_0
          split: test
          args:
            language: es
        metrics:
          - name: WER
            type: wer
            value: 9.2
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Mozilla Common Voice 10.0 (Dev)
          type: mozilla-foundation/common_voice_10_0
          split: validation
          args:
            language: es
        metrics:
          - name: WER
            type: wer
            value: 8.02
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: CIEMPIESS-TEST
          type: ciempiess/ciempiess_test
          split: test
          args:
            language: es
        metrics:
          - name: WER
            type: wer
            value: 11.17
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: 1997 Spanish Broadcast News Speech (HUB4-NE)
          type: HUB4NE_LDC98S74
          split: test
          args:
            language: es
        metrics:
          - name: WER
            type: wer
            value: 7.48
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: CALLHOME Spanish Speech (Test)
          type: callhome_LDC96S35
          split: test
          args:
            language: es
        metrics:
          - name: WER
            type: wer
            value: 39.12
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: CALLHOME Spanish Speech (Dev)
          type: callhome_LDC96S35
          split: validation
          args:
            language: es
        metrics:
          - name: WER
            type: wer
            value: 40.39

wav2vec2-large-xlsr-53-spanish-ep5-944h

The "wav2vec2-large-xlsr-53-spanish-ep5-944h" is an acoustic model suitable for Automatic Speech Recognition in Spanish. It is the result of fine-tuning the model "facebook/wav2vec2-large-xlsr-53" for 5 epochs with around 944 hours of Spanish data gathered or developed by the CIEMPIESS-UNAM Project since 2012. Most of the data is available at the the CIEMPIESS-UNAM Project homepage http://www.ciempiess.org/. The rest can be found in public repositories such as LDC or OpenSLR

The specific list of corpora used to fine-tune the model is:

The fine-tuning process was performed during November (2022) in the servers of the Language and Voice Lab (https://lvl.ru.is/) at Reykjavík University (Iceland) by Carlos Daniel Hernández Mena.

Evaluation 

import torch
from transformers import Wav2Vec2Processor
from transformers import Wav2Vec2ForCTC

#Load the processor and model.
MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-spanish-ep5-944h"
processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)

#Load the dataset
from datasets import load_dataset, load_metric, Audio
ds=load_dataset("ciempiess/ciempiess_test", split="test")

#Downsample to 16kHz
ds = ds.cast_column("audio", Audio(sampling_rate=16_000))

#Process the dataset
def prepare_dataset(batch):
    audio = batch["audio"]
    #Batched output is "un-batched" to ensure mapping is correct
    batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0]
    with processor.as_target_processor():
        batch["labels"] = processor(batch["normalized_text"]).input_ids
    return batch
ds = ds.map(prepare_dataset, remove_columns=ds.column_names,num_proc=1)

#Define the evaluation metric
import numpy as np
wer_metric = load_metric("wer")
def compute_metrics(pred):
    pred_logits = pred.predictions
    pred_ids = np.argmax(pred_logits, axis=-1)
    pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
    pred_str = processor.batch_decode(pred_ids)
    #We do not want to group tokens when computing the metrics
    label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
    wer = wer_metric.compute(predictions=pred_str, references=label_str)
    return {"wer": wer}

#Do the evaluation (with batch_size=1)
model = model.to(torch.device("cuda"))
def map_to_result(batch):
    with torch.no_grad():
        input_values = torch.tensor(batch["input_values"], device="cuda").unsqueeze(0)
        logits = model(input_values).logits
    pred_ids = torch.argmax(logits, dim=-1)
    batch["pred_str"] = processor.batch_decode(pred_ids)[0]
    batch["sentence"] = processor.decode(batch["labels"], group_tokens=False)
    return batch
results = ds.map(map_to_result,remove_columns=ds.column_names)

#Compute the overall WER now.
print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["sentence"])))

Test Result: 0.112

BibTeX entry and citation info

When publishing results based on these models please refer to:

@misc{mena2022xlrs53spanish,
      title={Acoustic Model in Spanish: wav2vec2-large-xlsr-53-spanish-ep5-944h.}, 
      author={Hernandez Mena, Carlos Daniel},
      year={2022},
      url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-spanish-ep5-944h},
}

Acknowledgements

The author wants to thank to the social service program "Desarrollo de Tecnologías del Habla" at the Facultad de Ingeniería (FI) of the Universidad Nacional Autónoma de México (UNAM). He also thanks to the social service students for all the hard work.

Special thanks to Jón Guðnason, head of the Language and Voice Lab for providing computational power to make this model possible. The author also thanks to the "Language Technology Programme for Icelandic 2019-2023" which is managed and coordinated by Almannarómur, and it is funded by the Icelandic Ministry of Education, Science and Culture.