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--- |
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language: es |
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datasets: |
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- ciempiess/ciempiess_light |
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- ciempiess/ciempiess_balance |
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- ciempiess/ciempiess_fem |
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- common_voice |
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- hub4ne_es_LDC98S74 |
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- callhome_es_LDC96S35 |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- spanish |
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- xlrs-53-spanish |
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- ciempiess |
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- cimpiess-unam |
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license: cc-by-4.0 |
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model-index: |
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- name: wav2vec2-large-xlsr-53-spanish-ep5-944h |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Mozilla Common Voice 10.0 (Test) |
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type: mozilla-foundation/common_voice_10_0 |
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split: test |
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args: |
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language: es |
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metrics: |
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- name: WER |
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type: wer |
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value: 9.20 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Mozilla Common Voice 10.0 (Dev) |
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type: mozilla-foundation/common_voice_10_0 |
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split: validation |
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args: |
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language: es |
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metrics: |
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- name: WER |
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type: wer |
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value: 8.02 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: CIEMPIESS-TEST |
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type: ciempiess/ciempiess_test |
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split: test |
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args: |
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language: es |
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metrics: |
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- name: WER |
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type: wer |
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value: 11.17 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: 1997 Spanish Broadcast News Speech (HUB4-NE) |
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type: HUB4NE_LDC98S74 |
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split: test |
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args: |
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language: es |
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metrics: |
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- name: WER |
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type: wer |
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value: 7.48 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: CALLHOME Spanish Speech (Test) |
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type: callhome_LDC96S35 |
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split: test |
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args: |
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language: es |
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metrics: |
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- name: WER |
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type: wer |
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value: 39.12 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: CALLHOME Spanish Speech (Dev) |
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type: callhome_LDC96S35 |
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split: validation |
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args: |
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language: es |
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metrics: |
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- name: WER |
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type: wer |
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value: 40.39 |
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--- |
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# wav2vec2-large-xlsr-53-spanish-ep5-944h |
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**Paper:** [The state of end-to-end systems for Mexican Spanish speech recognition](http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/viewFile/6485/3892) |
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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](https://huggingface.co/ciempiess) 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](https://www.ldc.upenn.edu/) or [OpenSLR](https://openslr.org/) |
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The specific list of corpora used to fine-tune the model is: |
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- [CIEMPIESS-LIGHT (18h25m)](https://catalog.ldc.upenn.edu/LDC2017S23) |
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- [CIEMPIESS-BALANCE (18h20m)](https://catalog.ldc.upenn.edu/LDC2018S11) |
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- [CIEMPIESS-FEM (13h54m)](https://catalog.ldc.upenn.edu/LDC2019S07) |
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- [CHM150 (1h38m)](https://catalog.ldc.upenn.edu/LDC2016S04) |
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- [TEDX_SPANISH (24h29m)](https://openslr.org/67/) |
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- [LIBRIVOX_SPANISH (73h01m)](https://catalog.ldc.upenn.edu/LDC2020S01) |
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- [WIKIPEDIA_SPANISH (25h37m)](https://catalog.ldc.upenn.edu/LDC2021S07) |
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- [VOXFORGE_SPANISH (49h42m)](http://www.voxforge.org/es) |
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- [MOZILLA COMMON VOICE 10.0 (320h22m)](https://commonvoice.mozilla.org/es) |
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- [HEROICO (16h33m)](https://catalog.ldc.upenn.edu/LDC2006S37) |
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- [LATINO-40 (6h48m)](https://catalog.ldc.upenn.edu/LDC95S28) |
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- [CALLHOME_SPANISH (13h22m)](https://catalog.ldc.upenn.edu/LDC96S35) |
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- [HUB4NE_SPANISH (31h41m)](https://catalog.ldc.upenn.edu/LDC98S74) |
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- [FISHER_SPANISH (127h22m)](https://catalog.ldc.upenn.edu/LDC2010S01) |
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- [Chilean Spanish speech data set (7h08m)](https://openslr.org/71/) |
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- [Colombian Spanish speech data set (7h34m)](https://openslr.org/72/) |
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- [Peruvian Spanish speech data set (9h13m)](https://openslr.org/73/) |
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- [Argentinian Spanish speech data set (8h01m)](https://openslr.org/61/) |
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- [Puerto Rico Spanish speech data set (1h00m)](https://openslr.org/74/) |
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- [MediaSpeech Spanish (10h00m)](https://openslr.org/108/) |
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- [DIMEX100-LIGHT (6h09m)](https://turing.iimas.unam.mx/~luis/DIME/CORPUS-DIMEX.html) |
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- [DIMEX100-NIÑOS (08h09m)](https://turing.iimas.unam.mx/~luis/DIME/CORPUS-DIMEX.html) |
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- [GOLEM-UNIVERSUM (00h10m)](https://turing.iimas.unam.mx/~luis/DIME/CORPUS-DIMEX.html) |
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- [GLISSANDO (6h40m)](https://glissando.labfon.uned.es/es) |
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- TELE_con_CIENCIA (28h16m) **Unplished Material** |
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- UNSHAREABLE MATERIAL (118h22m) **Not available for sharing** |
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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. |
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# Evaluation |
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```python |
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import torch |
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from transformers import Wav2Vec2Processor |
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from transformers import Wav2Vec2ForCTC |
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#Load the processor and model. |
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MODEL_NAME="carlosdanielhernandezmena/wav2vec2-large-xlsr-53-spanish-ep5-944h" |
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processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME) |
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME) |
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#Load the dataset |
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from datasets import load_dataset, load_metric, Audio |
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ds=load_dataset("ciempiess/ciempiess_test", split="test") |
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#Downsample to 16kHz |
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ds = ds.cast_column("audio", Audio(sampling_rate=16_000)) |
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#Process the dataset |
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def prepare_dataset(batch): |
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audio = batch["audio"] |
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#Batched output is "un-batched" to ensure mapping is correct |
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batch["input_values"] = processor(audio["array"], sampling_rate=audio["sampling_rate"]).input_values[0] |
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with processor.as_target_processor(): |
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batch["labels"] = processor(batch["normalized_text"]).input_ids |
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return batch |
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ds = ds.map(prepare_dataset, remove_columns=ds.column_names,num_proc=1) |
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|
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#Define the evaluation metric |
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import numpy as np |
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wer_metric = load_metric("wer") |
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def compute_metrics(pred): |
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pred_logits = pred.predictions |
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pred_ids = np.argmax(pred_logits, axis=-1) |
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pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id |
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pred_str = processor.batch_decode(pred_ids) |
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#We do not want to group tokens when computing the metrics |
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label_str = processor.batch_decode(pred.label_ids, group_tokens=False) |
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wer = wer_metric.compute(predictions=pred_str, references=label_str) |
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return {"wer": wer} |
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|
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#Do the evaluation (with batch_size=1) |
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model = model.to(torch.device("cuda")) |
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def map_to_result(batch): |
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with torch.no_grad(): |
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input_values = torch.tensor(batch["input_values"], device="cuda").unsqueeze(0) |
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logits = model(input_values).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_str"] = processor.batch_decode(pred_ids)[0] |
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batch["sentence"] = processor.decode(batch["labels"], group_tokens=False) |
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return batch |
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results = ds.map(map_to_result,remove_columns=ds.column_names) |
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#Compute the overall WER now. |
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print("Test WER: {:.3f}".format(wer_metric.compute(predictions=results["pred_str"], references=results["sentence"]))) |
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``` |
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**Test Result**: 0.112 |
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# BibTeX entry and citation info |
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*When publishing results based on these models please refer to:* |
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```bibtex |
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@misc{mena2022xlrs53spanish, |
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title={Acoustic Model in Spanish: wav2vec2-large-xlsr-53-spanish-ep5-944h.}, |
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author={Hernandez Mena, Carlos Daniel}, |
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url={https://huggingface.co/carlosdanielhernandezmena/wav2vec2-large-xlsr-53-spanish-ep5-944h}, |
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year={2022} |
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} |
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``` |
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# Acknowledgements |
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|
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The author wants to thank to the social service program ["Desarrollo de Tecnologías del Habla"](http://profesores.fi-b.unam.mx/carlos_mena/servicio.html) at the [Facultad de Ingeniería (FI)](https://www.ingenieria.unam.mx/) of the [Universidad Nacional Autónoma de México (UNAM)](https://www.unam.mx/). He also thanks to the social service students for all the hard work. |
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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. |
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