codeceejay
/

HIYACCENT_Wav2Vec2

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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: 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: 21.53
-       - name: Test CER
-         type: cer
-         value: 9.66
----
-# Wav2vec2-Large-English
-Fine-tuned [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) 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.
-This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
-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...
-Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library:
-```python
-from asrecognition import ASREngine
-asr = ASREngine("fr", model_path="jonatasgrosman/wav2vec2-large-english")
-audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
-transcriptions = asr.transcribe(audio_paths)
-```
-Writing your own inference script:
-```python
-import torch
-import librosa
-from datasets import load_dataset
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-LANG_ID = "en"
-MODEL_ID = "jonatasgrosman/wav2vec2-large-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." | SHELL BE ALL RIGHT |
-| SIX | SIX |
-| "ALL'S WELL THAT ENDS WELL." | ALLAS WELL THAT ENDS WELL |
-| DO YOU MEAN IT? | W 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 REGRESTION |
-| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOW IS MOSILLA GOING TO BANDL AND BE WHIT IS LIKE QU AND QU |
-| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RUSTION AS HAME AK AN THE POT |
-| 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 GUCE IS SAUCE 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 (en) 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-english"
-DEVICE = "cuda"
-CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
-                   "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
-                   "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
-                   "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
-                   "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
-test_dataset = load_dataset("common_voice", LANG_ID, 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["path"], sr=16_000)
-    batch["speech"] = speech_array
-    batch["sentence"] = re.sub(chars_to_ignore_regex, "", 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 2021-06-17). 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.
-| Model | WER | CER |
-| ------------- | ------------- | ------------- |
-| jonatasgrosman/wav2vec2-large-xlsr-53-english | **18.98%** | **8.29%** |
-| jonatasgrosman/wav2vec2-large-english | 21.53% | 9.66% |
-| facebook/wav2vec2-large-960h-lv60-self | 22.03% | 10.39% |
-| facebook/wav2vec2-large-960h-lv60 | 23.97% | 11.14% |
-| boris/xlsr-en-punctuation | 29.10% | 10.75% |
-| facebook/wav2vec2-large-960h | 32.79% | 16.03% |
-| 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% |
-## Citation
-If you want to cite this model you can use this:
-```bibtex
-@misc{grosman2021wav2vec2-large-english,
-  title={Wav2Vec2 English by Jonatas Grosman},
-  author={Grosman, Jonatas},
-  publisher={Hugging Face},
-  journal={Hugging Face Hub},
-  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-english}},
-  year={2021}
-}
-```