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Model: Edresson/wav2vec2-large-xlsr-coraa-portuguese
Source: Original Platform

README.md

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+---
+language: pt
+datasets:
+- CORAA 
+metrics:
+- wer
+tags:
+- audio
+- speech
+- wav2vec2
+- pt
+- portuguese-speech-corpus
+- automatic-speech-recognition
+- hf-asr-leaderboard
+- speech
+- PyTorch
+license: apache-2.0
+model-index:
+- name: Edresson Casanova XLSR Wav2Vec2 Large 53 Portuguese 
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: CORAA
+      type: CORAA
+      args: pt
+    metrics:
+       - name: Test CORAA WER
+         type: wer
+         value: 25.26
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice 7
+      type: mozilla-foundation/common_voice_7_0
+      args: pt
+    metrics:
+       - name: Test WER on Common Voice 7
+         type: wer
+         value: 20.08        
+---
+
+# Wav2vec 2.0 trained with CORAA Portuguese Dataset
+
+This a the demonstration of a fine-tuned Wav2vec model for Portuguese using the following  [CORAA dataset](https://github.com/nilc-nlp/CORAA)
+
+
+
+# Use this model
+
+```python
+
+from transformers import AutoTokenizer, Wav2Vec2ForCTC
+  
+tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese")
+
+model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-xlsr-coraa-portuguese")
+```
+# Results
+For the results check the [CORAA article](https://arxiv.org/abs/2110.15731)
+
+# Example test with Common Voice Dataset
+
+
+```python
+dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
+
+resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
+
+def map_to_array(batch):
+    speech, _ = torchaudio.load(batch["path"])
+    batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
+    batch["sampling_rate"] = resampler.new_freq
+    batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
+    return batch
+```
+
+```python
+ds = dataset.map(map_to_array)
+result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
+print(wer.compute(predictions=result["predicted"], references=result["target"]))
+```