add sample audio file

.ipynb_checkpoints/README-checkpoint.md

@@ -0,0 +1,83 @@
+---
+language: vi
+datasets:
+- common_voice
+- librispeech_asr
+- how2
+- must-c-v1
+- must-c-v2
+- europarl
+- tedlium
+tags:
+- audio
+- automatic-speech-recognition
+license: cc-by-nc-4.0
+---
+
+# Fine-Tune Wav2Vec2 large model for English ASR
+
+
+### Data for fine-tune
+
+| Dataset      | Duration in hours |
+|--------------|-------------------|
+| Common Voice |              1667 |
+| Europarl     |                85 |
+| How2         |               356 |
+| Librispeech  |               936 |
+| MuST-C v1    |               407 |
+| MuST-C v2    |               482 |
+| Tedlium      |               482 |
+
+
+### Evaluation result
+
+| Dataset     | Duration in hours | WER w/o LM | WER with LM |
+|-------------|-------------------|------------|-------------|
+| Librispeech |        5.4        |     2.9    |     1.1     |
+|   Tedlium   |        2.6        |     7.9    |     5.4     |
+
+
+### Usage
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1FAhtGvjRdHT4W0KeMdMMlL7sm6Hbe7dv?usp=sharing)
+
+```python
+from transformers.file_utils import cached_path, hf_bucket_url
+from importlib.machinery import SourceFileLoader
+from transformers import Wav2Vec2ProcessorWithLM
+from IPython.lib.display import Audio
+import torchaudio
+import torch
+
+# Load model & processor
+model_name = "nguyenvulebinh/iwslt-asr-wav2vec-large"
+model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
+processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)
+
+# Load an example audio (16k)
+audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="tst_2010_sample.wav")))
+input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')
+
+# Infer
+output = model(**input_data)
+
+# Output transcript without LM
+print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))
+# and of course there's teams that have a lot more tada structures and among the best are recent graduates of kindergarten
+
+# Output transcript with LM
+print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)
+# and of course there are teams that have a lot more ta da structures and among the best are recent graduates of kindergarten
+```
+
+### Model Parameters License
+
+The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+
+### Contact 
+
+nguyenvulebinh@gmail.com
+
+[![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)

README.md

@@ -1,3 +1,83 @@
 ---
-license: cc-by-4.0
+language: vi
+datasets:
+- common_voice
+- librispeech_asr
+- how2
+- must-c-v1
+- must-c-v2
+- europarl
+- tedlium
+tags:
+- audio
+- automatic-speech-recognition
+license: cc-by-nc-4.0
 ---
+
+# Fine-Tune Wav2Vec2 large model for English ASR
+
+
+### Data for fine-tune
+
+| Dataset      | Duration in hours |
+|--------------|-------------------|
+| Common Voice |              1667 |
+| Europarl     |                85 |
+| How2         |               356 |
+| Librispeech  |               936 |
+| MuST-C v1    |               407 |
+| MuST-C v2    |               482 |
+| Tedlium      |               482 |
+
+
+### Evaluation result
+
+| Dataset     | Duration in hours | WER w/o LM | WER with LM |
+|-------------|-------------------|------------|-------------|
+| Librispeech |        5.4        |     2.9    |     1.1     |
+|   Tedlium   |        2.6        |     7.9    |     5.4     |
+
+
+### Usage
+
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1FAhtGvjRdHT4W0KeMdMMlL7sm6Hbe7dv?usp=sharing)
+
+```python
+from transformers.file_utils import cached_path, hf_bucket_url
+from importlib.machinery import SourceFileLoader
+from transformers import Wav2Vec2ProcessorWithLM
+from IPython.lib.display import Audio
+import torchaudio
+import torch
+
+# Load model & processor
+model_name = "nguyenvulebinh/iwslt-asr-wav2vec-large"
+model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name)
+processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name)
+
+# Load an example audio (16k)
+audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="tst_2010_sample.wav")))
+input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt')
+
+# Infer
+output = model(**input_data)
+
+# Output transcript without LM
+print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy()))
+# and of course there's teams that have a lot more tada structures and among the best are recent graduates of kindergarten
+
+# Output transcript with LM
+print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text)
+# and of course there are teams that have a lot more ta da structures and among the best are recent graduates of kindergarten
+```
+
+### Model Parameters License
+
+The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode
+
+
+### Contact 
+
+nguyenvulebinh@gmail.com
+
+[![Follow](https://img.shields.io/twitter/follow/nguyenvulebinh?style=social)](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)