Update README.md

README.md

@@ -17,7 +17,7 @@ tags:
 - xlsr-fine-tuning-week
 license: apache-2.0
 model-index:
-- name: XLSR Wav2Vec2 English by Jonatas Grosman
+- name: XLSR Wav2Vec2 English
   results:
   - task:
       name: Automatic Speech Recognition
@@ -59,6 +59,7 @@ model-index:
     - name: Dev CER (+LM)
       type: cer
       value: 11.01
+library_name: transformers
 ---
 
 # Fine-tuned XLSR-53 large model for speech recognition in English
@@ -66,100 +67,4 @@ model-index:
 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English using the train and validation splits of [Common Voice 6.1](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 [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
-
-```python
-from huggingsound import SpeechRecognitionModel
-
-model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english")
-audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
-
-transcriptions = model.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-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'LL BE ALL RIGHT |
-| SIX | SIX |
-| "ALL'S WELL THAT ENDS WELL." | ALL AS 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? | HOW IS MOSLILLAR GOING TO HANDLE ANDBEWOOTH HIS LIKE Q AND Q |
-| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RUSTIAN WASTIN PAN ONTE BATTLY |
-| 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 GUICE 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
-
-1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test`
-
-```bash
-python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset mozilla-foundation/common_voice_6_0 --config en --split test
-```
-
-2. To evaluate on `speech-recognition-community-v2/dev_data`
-
-```bash
-python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset speech-recognition-community-v2/dev_data --config en --split validation --chunk_length_s 5.0 --stride_length_s 1.0
-```
-
-## Citation
-If you want to cite this model you can use this:
-
-```bibtex
-@misc{grosman2021xlsr53-large-english,
-  title={Fine-tuned {XLSR}-53 large model for speech recognition in {E}nglish},
-  author={Grosman, Jonatas},
-  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english}},
-  year={2021}
-}
-```