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README.md

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+---
+language: pt
+datasets:
+- common_voice
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: GChhablani Wav2Vec2 Large 53 Portugese
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice pt
+      type: common_voice
+      args: pt
+    metrics:
+       - name: Test WER
+         type: wer
+         value: {wer_result_on_test}
+---
+
+# Wav2Vec2-Large-XLSR-53-Portuguese
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Portuguese using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset. 
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+## Usage
+
+The model can be used directly (without a language model) as follows:
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+test_dataset = load_dataset("common_voice", "pt", split="test[:2%]")
+
+processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt")
+model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt")
+
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+  	speech_array, sampling_rate = torchaudio.load(batch["path"])
+  	batch["speech"] = resampler(speech_array).squeeze().numpy()
+  	return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+with torch.no_grad():
+  	logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+
+predicted_ids = torch.argmax(logits, dim=-1)
+
+print("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset["sentence"][:2])
+```
+
+
+## Evaluation
+
+The model can be evaluated as follows on the Portuguese test data of Common Voice.
+
+
+```python
+import torch
+import torchaudio
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+
+test_dataset = load_dataset("common_voice", "pt", split="test") #TODO: replace {lang_id} in your language code here. Make sure the code is one of the *ISO codes* of [this](https://huggingface.co/languages) site.
+wer = load_metric("wer")
+
+processor = Wav2Vec2Processor.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
+model = Wav2Vec2ForCTC.from_pretrained("gchhablani/wav2vec2-large-xlsr-pt") #TODO: replace {model_id} with your model id. The model id consists of {your_username}/{your_modelname}, *e.g.* `elgeish/wav2vec2-large-xlsr-53-arabic`
+model.to("cuda")
+
+chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“\'\�]'
+resampler = torchaudio.transforms.Resample(48_000, 16_000)
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def speech_file_to_array_fn(batch):
+	  batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
+		speech_array, sampling_rate = torchaudio.load(batch["path"])
+  	batch["speech"] = resampler(speech_array).squeeze().numpy()
+  	return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+
+# Preprocessing the datasets.
+# We need to read the aduio files as arrays
+def evaluate(batch):
+  	inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+
+  	with torch.no_grad():
+  		logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
+
+  	pred_ids = torch.argmax(logits, dim=-1)
+  	batch["pred_strings"] = processor.batch_decode(pred_ids)
+  	return batch
+
+result = test_dataset.map(evaluate, batched=True, batch_size=8)
+
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+```
+
+**Test Result**: XX.XX %  # TODO: write output of print here. IMPORTANT: Please remember to also replace {wer_result_on_test} at the top of with this value here. tags.
+
+
+## Training
+
+The Common Voice `train` and `validation` datasets were used for training. The script used for training can be found [here](https://github.com/jqueguiner/wav2vec2-sprint/blob/main/run_common_voice.py).
+ The parameters passed were:
+
+```bash
+#!/usr/bin/env bash
+python run_common_voice.py \
+    --model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
+    --dataset_config_name="pt" \
+    --output_dir=/workspace/output_models/pt/wav2vec2-large-xlsr-pt \
+    --cache_dir=/workspace/data \
+    --overwrite_output_dir \
+    --num_train_epochs="30" \
+    --per_device_train_batch_size="32" \
+    --per_device_eval_batch_size="32" \
+    --evaluation_strategy="steps" \
+    --learning_rate="3e-4" \
+    --warmup_steps="500" \
+    --fp16 \
+    --freeze_feature_extractor \
+    --save_steps="500" \
+    --eval_steps="500" \
+    --save_total_limit="1" \
+    --logging_steps="500" \
+    --group_by_length \
+    --feat_proj_dropout="0.0" \
+    --layerdrop="0.1" \
+    --gradient_checkpointing \
+    --do_train --do_eval \
+```