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README.md

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+---
+language: en
+datasets:
+- common_voice
+metrics:
+- wer
+- cer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: XLSR Wav2Vec2 English by Jonatas Grosman
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice en
+      type: common_voice
+      args: en
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 39.59
+       - name: Test CER
+         type: cer
+         value: 18.18
+---
+
+# Wav2Vec2-Large-XLSR-53-English
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English using the [Common Voice](https://huggingface.co/datasets/common_voice).
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
+
+## Usage
+
+The model can be used directly (without a language model) as follows:
+
+```python
+import torch
+import librosa
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+LANG_ID = "en"
+MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-english"
+SAMPLES = 10
+
+test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
+
+processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+
+# Preprocessing the datasets.
+# We need to read the audio files as arrays
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
+    batch["speech"] = speech_array
+    batch["sentence"] = batch["sentence"].upper()
+    return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset["speech"], 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)
+predicted_sentences = processor.batch_decode(predicted_ids)
+
+for i, predicted_sentence in enumerate(predicted_sentences):
+    print("-" * 100)
+    print("Reference:", test_dataset[i]["sentence"])
+    print("Prediction:", predicted_sentence)
+```
+
+| Reference  | Prediction |
+| ------------- | ------------- |
+| "SHE'LL BE ALL RIGHT." | SHE'LD BE ALL RIGHT |
+| SIX | SIX |
+| "ALL'S WELL THAT ENDS WELL." | ALL IS WELL THAT ENDS WELL |
+| DO YOU MEAN IT? | DO YOU MEAN IT |
+| THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE, BUT STILL CAUSES REGRESSIONS. | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE BUT STILL CAUSES REGRESSION |
+| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOWIS MOCILE ARE GOING TO HANDLE AMBIGUITIES LIKE KU AND KU |
+| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RISSHON WAS INCAN IN THE BAK TE |
+| NO ONE NEAR THE REMOTE MACHINE YOU COULD RING? | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING |
+| SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER. | SAUCE FOR THE GUISE IS SAUCED FOR THE GONDER |
+| GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. | GRAFS STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD |
+
+## Evaluation
+
+The model can be evaluated as follows on the English test data of Common Voice.
+
+```python
+import torch
+import re
+import librosa
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+LANG_ID = "en"
+MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-english"
+DEVICE = "cuda"
+
+CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
+                   "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
+                   "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
+                   "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
+                   "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
+
+test_dataset = load_dataset("common_voice", LANG_ID, split="test")
+
+# uncomment the following lines to eval using other datasets
+# test_dataset = load_dataset("librispeech_asr", "clean", split="test")
+# test_dataset = load_dataset("librispeech_asr", "other", split="test")
+# test_dataset = load_dataset("timit_asr", split="test")
+
+wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
+cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
+
+chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
+
+processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+model.to(DEVICE)
+
+# Preprocessing the datasets.
+# We need to read the audio files as arrays
+def speech_file_to_array_fn(batch):
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        speech_array, sampling_rate = librosa.load(batch["file"] if "file" in batch else batch["path"], sr=16_000)
+    batch["speech"] = speech_array
+    batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["text"] if "text" in batch else batch["sentence"]).upper()
+    return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+
+# Preprocessing the datasets.
+# We need to read the audio 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(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).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)
+
+predictions = [x.upper() for x in result["pred_strings"]]
+references = [x.upper() for x in result["sentence"]]
+
+print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
+print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
+```
+
+**Test Result**:
+
+In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-20). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
+
+---
+
+**Common Voice**
+
+| Model | WER | CER |
+| ------------- | ------------- | ------------- |
+| jonatasgrosman/wav2vec2-large-xlsr-53-english | **19.18%** | **8.25%** |
+| jonatasgrosman/wav2vec2-large-english | 21.16% | 9.53% |
+| facebook/wav2vec2-large-960h-lv60-self | 22.03% | 10.39% |
+| facebook/wav2vec2-large-960h-lv60 | 23.97% | 11.14% |
+| facebook/wav2vec2-large-960h | 32.79% | 16.03% |
+| boris/xlsr-en-punctuation | 34.81% | 15.51% |
+| facebook/wav2vec2-base-960h | 39.86% | 19.89% |
+| facebook/wav2vec2-base-100h | 51.06% | 25.06% |
+| elgeish/wav2vec2-large-lv60-timit-asr | 59.96% | 34.28% |
+| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 66.41% | 36.76% |
+| elgeish/wav2vec2-base-timit-asr | 68.78% | 36.81% |
+
+---
+
+**LibriSpeech (clean)**
+
+| Model | WER | CER |
+| ------------- | ------------- | ------------- |
+| facebook/wav2vec2-large-960h-lv60-self | **1.86%** | **0.54%** |
+| facebook/wav2vec2-large-960h-lv60 | 2.15% | 0.61% |
+| facebook/wav2vec2-large-960h | 2.82% | 0.84% |
+| facebook/wav2vec2-base-960h | 3.44% | 1.06% |
+| facebook/wav2vec2-base-100h | 6.26% | 2.00% |
+| jonatasgrosman/wav2vec2-large-xlsr-53-english | 6.97% | 2.02% |
+| jonatasgrosman/wav2vec2-large-english | 8.00% | 2.55% |
+| elgeish/wav2vec2-large-lv60-timit-asr | 15.53% | 4.93% |
+| boris/xlsr-en-punctuation | 19.28% | 6.45% |
+| elgeish/wav2vec2-base-timit-asr | 29.19% | 8.38% |
+| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 31.82% | 12.41% |
+
+---
+
+**LibriSpeech (other)**
+
+| Model | WER | CER |
+| ------------- | ------------- | ------------- |
+| facebook/wav2vec2-large-960h-lv60-self | **3.89%** | **1.40%** |
+| facebook/wav2vec2-large-960h-lv60 | 4.45% | 1.56% |
+| facebook/wav2vec2-large-960h | 6.49% | 2.52% |
+| facebook/wav2vec2-base-960h | 8.90% | 3.55% |
+| jonatasgrosman/wav2vec2-large-xlsr-53-english | 11.75% | 4.23% |
+| jonatasgrosman/wav2vec2-large-english | 13.62% | 5.24% |
+| facebook/wav2vec2-base-100h | 13.97% | 5.51% |
+| boris/xlsr-en-punctuation | 26.40% | 10.11% |
+| elgeish/wav2vec2-large-lv60-timit-asr | 28.39% | 12.08% |
+| elgeish/wav2vec2-base-timit-asr | 42.04% | 15.57% |
+| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 45.19% | 20.32% |
+
+---
+
+**TIMIT**
+
+| Model | WER | CER |
+| ------------- | ------------- | ------------- |
+| facebook/wav2vec2-large-960h-lv60-self | **5.17%** | **1.33%** |
+| facebook/wav2vec2-large-960h-lv60 | 6.24% | 1.54% |
+| facebook/wav2vec2-large-960h | 9.63% | 2.19% |
+| facebook/wav2vec2-base-960h | 11.48% | 2.76% |
+| jonatasgrosman/wav2vec2-large-xlsr-53-english | 11.93% | 3.50% |
+| elgeish/wav2vec2-large-lv60-timit-asr | 13.83% | 4.36% |
+| jonatasgrosman/wav2vec2-large-english | 13.91% | 4.01% |
+| facebook/wav2vec2-base-100h | 16.75% | 4.79% |
+| elgeish/wav2vec2-base-timit-asr | 25.40% | 8.16% |
+| boris/xlsr-en-punctuation | 25.93% | 9.99% |
+| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 51.08% | 19.84% |

config.json

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+{
+  "_name_or_path": "facebook/wav2vec2-large-xlsr-53",
+  "activation_dropout": 0.05,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForCTC"
+  ],
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "conv_bias": true,
+  "conv_dim": [
+    512,
+    512,
+    512,
+    512,
+    512,
+    512,
+    512
+  ],
+  "conv_kernel": [
+    10,
+    3,
+    3,
+    3,
+    3,
+    2,
+    2
+  ],
+  "conv_stride": [
+    5,
+    2,
+    2,
+    2,
+    2,
+    2,
+    2
+  ],
+  "ctc_loss_reduction": "mean",
+  "ctc_zero_infinity": true,
+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
+  "feat_extract_dropout": 0.0,
+  "feat_extract_norm": "layer",
+  "feat_proj_dropout": 0.05,
+  "final_dropout": 0.0,
+  "gradient_checkpointing": true,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.05,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-05,
+  "layerdrop": 0.05,
+  "mask_channel_length": 10,
+  "mask_channel_min_space": 1,
+  "mask_channel_other": 0.0,
+  "mask_channel_prob": 0.0,
+  "mask_channel_selection": "static",
+  "mask_feature_length": 10,
+  "mask_feature_prob": 0.0,
+  "mask_time_length": 10,
+  "mask_time_min_space": 1,
+  "mask_time_other": 0.0,
+  "mask_time_prob": 0.05,
+  "mask_time_selection": "static",
+  "model_type": "wav2vec2",
+  "num_attention_heads": 16,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "transformers_version": "4.5.0.dev0",
+  "vocab_size": 33
+}

preprocessor_config.json

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+{
+  "do_normalize": true,
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3ba6ad16f6ecfcadd07ca5bc3353c3168665bee9fbfb160fbc864a6a9f87ca58
+size 1262069143

special_tokens_map.json

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+{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

vocab.json

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+{"<pad>": 0, "<s>": 1, "</s>": 2, "<unk>": 3, "|": 4, "'": 5, "-": 6, "A": 7, "B": 8, "C": 9, "D": 10, "E": 11, "F": 12, "G": 13, "H": 14, "I": 15, "J": 16, "K": 17, "L": 18, "M": 19, "N": 20, "O": 21, "P": 22, "Q": 23, "R": 24, "S": 25, "T": 26, "U": 27, "V": 28, "W": 29, "X": 30, "Y": 31, "Z": 32}