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README.md

@@ -1,44 +1,61 @@
 ---
-language: "en"
-thumbnail:
+language:
+- en
+thumbnail: null
 pipeline_tag: automatic-speech-recognition
 tags:
 - CTC
-- Attention
 - pytorch
 - speechbrain
 - Transformer
-license: "apache-2.0"
+license: apache-2.0
 datasets:
-- commonvoice
+- commonvoice.14.0
 metrics:
 - wer
 - cer
+model-index:
+- name: asr-wav2vec2-commonvoice-14-en
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: CommonVoice Corpus 14.0 (English)
+      type: mozilla-foundation/common_voice_14.0
+      config: en
+      split: test
+      args:
+        language: en
+    metrics:
+    - name: Test WER
+      type: wer
+      value: '16.68'
 ---
 
 <iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe>
 <br/><br/>
 
-# wav2vec 2.0 with CTC/Attention trained on CommonVoice Italian (No LM)
+# wav2vec 2.0 with CTC trained on CommonVoice English (No LM)
 
 This repository provides all the necessary tools to perform automatic speech
-recognition from an end-to-end system pretrained on CommonVoice (Italian Language) within
+recognition from an end-to-end system pretrained on CommonVoice (English Language) within
 SpeechBrain. For a better experience, we encourage you to learn more about
-[SpeechBrain](https://speechbrain.github.io). 
+[SpeechBrain](https://speechbrain.github.io).
 
 The performance of the model is the following:
 
 | Release | Test CER | Test WER | GPUs |
-|:--------------:|:--------------:|:--------------:| :--------:|
-| 03-06-21 | 2.38 | 8.38 | 1xV100 32GB |
+|:-------------:|:--------------:|:--------------:| :--------:|
+| 15-08-23 | 7.92  | 16.86  | 1xV100 32GB |
 
 ## Pipeline description
 
 This ASR system is composed of 2 different but linked blocks:
-- Tokenizer (unigram) that transforms words into subword units and trained with
-the train transcriptions (train.tsv) of CommonVoice (EN).
-- Acoustic model (wav2vec2.0 + CTC/Attention). A pretrained wav2vec 2.0 model ([facebook/wav2vec2-large-it-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-it-voxpopuli)) is combined with two DNN layers and finetuned on CommonVoice En. 
-The obtained final acoustic representation is given to the CTC and attention decoders.
+- Tokenizer (unigram) that transforms words into unigrams and trained with
+the train transcriptions (train.tsv) of CommonVoice (en).  
+- Acoustic model (wav2vec2.0 + CTC). A pretrained wav2vec 2.0 model ([wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60)) is combined with two DNN layers and finetuned on CommonVoice DE. 
+The obtained final acoustic representation is given to the CTC decoder.
 
 The system is trained with recordings sampled at 16kHz (single channel).
 The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *transcribe_file* if needed.
@@ -54,13 +71,13 @@ pip install speechbrain transformers
 Please notice that we encourage you to read our tutorials and learn more about
 [SpeechBrain](https://speechbrain.github.io).
 
-### Transcribing your own audio files (in Italian)
+### Transcribing your own audio files (in English)
 
 ```python
-from speechbrain.pretrained import EncoderDecoderASR
+from speechbrain.pretrained import EncoderASR
 
-asr_model = EncoderDecoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-14-it", savedir="pretrained_models/asr-wav2vec2-commonvoice-14-it")
-asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-14-it/example-it.wav")
+asr_model = EncoderASR.from_hparams(source="speechbrain/asr-wav2vec2-commonvoice-14-en", savedir="pretrained_models/asr-wav2vec2-commonvoice-14-en")
+asr_model.transcribe_file("speechbrain/asr-wav2vec2-commonvoice-14-en/example-en.wav")
 
 ```
 ### Inference on GPU
@@ -85,15 +102,16 @@ pip install -e .
 
 3. Run Training:
 ```bash
-cd recipes/CommonVoice/ASR/seq2seq
-python train_with_wav2vec.py hparams/train_it_with_wav2vec.yaml --data_folder=your_data_folder
+cd recipes/CommonVoice/ASR/CTC/
+python train_with_wav2vec.py hparams/train_en_with_wav2vec.yaml --data_folder=your_data_folder
 ```
 
-You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1tjz6IZmVRkuRE97E7h1cXFoGTer7pT73?usp=sharing).
+You can find our training results (models, logs, etc) [here](https://www.dropbox.com/sh/ch10cnbhf1faz3w/AACdHFG65LC6582H0Tet_glTa?dl=0).
 
 ### Limitations
 The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
 
+
 # **About SpeechBrain**
 - Website: https://speechbrain.github.io/
 - Code: https://github.com/speechbrain/speechbrain/
@@ -113,4 +131,4 @@ Please, cite SpeechBrain if you use it for your research or business.
   primaryClass={eess.AS},
   note={arXiv:2106.04624}
 }
-```
+```

config.json

@@ -1,76 +1,69 @@
 {
-  "speechbrain_interface": "EncoderDecoderASR",
-  "activation_dropout": 0.0,
-  "apply_spec_augment": true,
-  "architectures": [
-    "Wav2Vec2Model"
-  ],
-  "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": "sum",
-  "ctc_zero_infinity": false,
-  "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": 0.1,
-  "final_dropout": 0.0,
-  "gradient_checkpointing": false,
-  "hidden_act": "gelu",
-  "hidden_dropout": 0.1,
-  "hidden_size": 1024,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_eps": 1e-05,
-  "layerdrop": 0.1,
-  "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": 0.075,
-  "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": "4.6.0.dev0",
-  "vocab_size": 32
-}
+    "speechbrain_interface": "EncoderASR",
+    "activation_dropout": 0.1,
+    "apply_spec_augment": true,
+    "architectures": [
+        "Wav2Vec2Model"
+    ],
+    "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": "sum",
+    "ctc_zero_infinity": false,
+    "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": 0.1,
+    "final_dropout": 0.1,
+    "gradient_checkpointing": false,
+    "hidden_act": "gelu",
+    "hidden_dropout": 0.1,
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 1024,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "layer_norm_eps": 1e-05,
+    "layerdrop": 0.1,
+    "mask_feature_length": 10,
+    "mask_feature_prob": 0.0,
+    "mask_time_length": 10,
+    "mask_time_prob": 0.05,
+    "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": "4.21.1",
+    "vocab_size": 32
+}

hyperparams.yaml

@@ -1,22 +1,24 @@
 # ################################
-# Model: wav2vec2 + DNN + CTC/Attention
+# Model: wav2vec2 + DNN + CTC
 # Augmentation: SpecAugment
-# Authors: Titouan Parcollet 2021
+# Authors: 
+# Sung-Lin Yeh 2021
+# Pooneh Mousavi 2023
 # ################################
 
-sample_rate: 16000
-wav2vec2_hub: facebook/wav2vec2-large-it-voxpopuli
-
 # BPE parameters
 token_type: unigram  # ["unigram", "bpe", "char"]
 character_coverage: 1.0
 
 # Model parameters
-activation: !name:torch.nn.LeakyReLU
-dnn_layers: 2
+# activation: !name:torch.nn.LeakyReLU
 dnn_neurons: 1024
-emb_size: 128
-dec_neurons: 1024
+wav2vec_output_dim: 1024
+dropout: 0.15
+
+sample_rate: 16000
+
+wav2vec2_hub: facebook/wav2vec2-large-lv60
 
 # Outputs
 output_neurons: 1000  # BPE size, index(blank/eos/bos) = 0
@@ -26,93 +28,63 @@ output_neurons: 1000  # BPE size, index(blank/eos/bos) = 0
 blank_index: 0
 bos_index: 1
 eos_index: 2
-min_decode_ratio: 0.0
-max_decode_ratio: 1.0
-beam_size: 10
-eos_threshold: 1.5
-using_max_attn_shift: True
-max_attn_shift: 140
-ctc_weight_decode: 0.0
-temperature: 1.50
-
-enc: !new:speechbrain.lobes.models.VanillaNN.VanillaNN
-    input_shape: [null, null, 1024]
-    activation: !ref <activation>
-    dnn_blocks: !ref <dnn_layers>
-    dnn_neurons: !ref <dnn_neurons>
+
+enc: !new:speechbrain.nnet.containers.Sequential
+  input_shape: [null, null, !ref <wav2vec_output_dim>]
+  linear1: !name:speechbrain.nnet.linear.Linear
+    n_neurons: !ref <dnn_neurons>
+    bias: True
+  bn1: !name:speechbrain.nnet.normalization.BatchNorm1d
+  activation: !new:torch.nn.LeakyReLU
+  drop: !new:torch.nn.Dropout
+    p: !ref <dropout>
+  linear2: !name:speechbrain.nnet.linear.Linear
+    n_neurons: !ref <dnn_neurons>
+    bias: True
+  bn2: !name:speechbrain.nnet.normalization.BatchNorm1d
+  activation2: !new:torch.nn.LeakyReLU
+  drop2: !new:torch.nn.Dropout
+    p: !ref <dropout>
+  linear3: !name:speechbrain.nnet.linear.Linear
+    n_neurons: !ref <dnn_neurons>
+    bias: True
+  bn3: !name:speechbrain.nnet.normalization.BatchNorm1d
+  activation3: !new:torch.nn.LeakyReLU
 
 wav2vec2: !new:speechbrain.lobes.models.huggingface_wav2vec.HuggingFaceWav2Vec2
-    source: !ref <wav2vec2_hub>
-    output_norm: True
-    freeze: True
-    save_path: model_checkpoints
-
-emb: !new:speechbrain.nnet.embedding.Embedding
-    num_embeddings: !ref <output_neurons>
-    embedding_dim: !ref <emb_size>
-
-dec: !new:speechbrain.nnet.RNN.AttentionalRNNDecoder
-    enc_dim: !ref <dnn_neurons>
-    input_size: !ref <emb_size>
-    rnn_type: gru
-    attn_type: location
-    hidden_size: 1024
-    attn_dim: 1024
-    num_layers: 1
-    scaling: 1.0
-    channels: 10
-    kernel_size: 100
-    re_init: True
-    dropout: 0.15
+  source: !ref <wav2vec2_hub>
+  output_norm: True
+  freeze: True
+  save_path: wav2vec2_checkpoint
 
 ctc_lin: !new:speechbrain.nnet.linear.Linear
-    input_size: !ref <dnn_neurons>
-    n_neurons: !ref <output_neurons>
-
-seq_lin: !new:speechbrain.nnet.linear.Linear
-    input_size: !ref <dec_neurons>
-    n_neurons: !ref <output_neurons>
+  input_size: !ref <dnn_neurons>
+  n_neurons: !ref <output_neurons>
 
 log_softmax: !new:speechbrain.nnet.activations.Softmax
-    apply_log: True
+  apply_log: True
 
 ctc_cost: !name:speechbrain.nnet.losses.ctc_loss
-    blank_index: !ref <blank_index>
-
-seq_cost: !name:speechbrain.nnet.losses.nll_loss
-    label_smoothing: 0.1
+  blank_index: !ref <blank_index>
 
 asr_model: !new:torch.nn.ModuleList
-    - [!ref <enc>, !ref <emb>, !ref <dec>, !ref <ctc_lin>, !ref <seq_lin>]
+    - [!ref <enc>, !ref <ctc_lin>]
 
 tokenizer: !new:sentencepiece.SentencePieceProcessor
 
 encoder: !new:speechbrain.nnet.containers.LengthsCapableSequential
     wav2vec2: !ref <wav2vec2>
     enc: !ref <enc>
-
-decoder: !new:speechbrain.decoders.S2SRNNBeamSearcher
-    embedding: !ref <emb>
-    decoder: !ref <dec>
-    linear: !ref <seq_lin>
-    ctc_linear: !ref <ctc_lin>
-    bos_index: !ref <bos_index>
-    eos_index: !ref <eos_index>
-    blank_index: !ref <blank_index>
-    min_decode_ratio: !ref <min_decode_ratio>
-    max_decode_ratio: !ref <max_decode_ratio>
-    beam_size: !ref <beam_size>
-    eos_threshold: !ref <eos_threshold>
-    using_max_attn_shift: !ref <using_max_attn_shift>
-    max_attn_shift: !ref <max_attn_shift>
-    temperature: !ref <temperature>
+    ctc_lin: !ref <ctc_lin>
 
 modules:
-    encoder: !ref <encoder>
-    decoder: !ref <decoder>
+  encoder: !ref <encoder>
+
+decoding_function: !name:speechbrain.decoders.ctc_greedy_decode
+    blank_id: !ref <blank_index>
 
 pretrainer: !new:speechbrain.utils.parameter_transfer.Pretrainer
-    loadables:
-        wav2vec2: !ref <wav2vec2>
-        asr: !ref <asr_model>
-        tokenizer: !ref <tokenizer>
+   loadables:
+      wav2vec2: !ref <wav2vec2>
+      asr: !ref <asr_model>
+      tokenizer: !ref <tokenizer>

preprocessor_config.json

@@ -1,9 +1,8 @@
 {
-  "do_normalize": true,
-  "feature_extractor_type": "Wav2Vec2FeatureExtractor",
-  "feature_size": 1,
-  "padding_side": "right",
-  "padding_value": 0,
-  "return_attention_mask": true,
-  "sampling_rate": 16000
-}
+    "do_normalize": true,
+    "feature_size": 1,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "return_attention_mask": true,
+    "sampling_rate": 16000
+}