valhalla commited on
Commit
6584d1a
1 Parent(s): 79dc10e
README.md ADDED
@@ -0,0 +1,120 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - de
4
+ - en
5
+ datasets:
6
+ - covost2
7
+ tags:
8
+ - audio
9
+ - speech-translation
10
+ - automatic-speech-recognition
11
+ license: MIT
12
+ ---
13
+
14
+
15
+ # S2T-SMALL-COVOST2-DE-EN-ST
16
+
17
+ `s2t-small-covost2-de-en-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST).
18
+ The S2T model was proposed in [this paper](https://arxiv.org/abs/2010.05171) and released in
19
+ [this repository](https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text)
20
+
21
+
22
+ ## Model description
23
+
24
+ S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech
25
+ Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are
26
+ fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the
27
+ transcripts/translations autoregressively.
28
+
29
+ ## Intended uses & limitations
30
+
31
+ This model can be used for end-to-end German speech to English text translation.
32
+ See the [model hub](https://huggingface.co/models?filter=speech_to_text) to look for other S2T checkpoints.
33
+
34
+
35
+ ### How to use
36
+
37
+ As this a standard sequence to sequence transformer model, you can use the `generate` method to generate the
38
+ transcripts by passing the speech features to the model.
39
+
40
+ *Note: The `Speech2TextProcessor` object uses [torchaudio](https://github.com/pytorch/audio) to extract the
41
+ filter bank features. Make sure to install the `torchaudio` package before running this example.*
42
+
43
+ You could either install those as extra speech dependancies with
44
+ `pip install transformers"[speech, sentencepiece]"` or install the packages seperatly
45
+ with `pip install torchaudio sentencepiece`.
46
+
47
+
48
+ ```python
49
+ import torch
50
+ from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
51
+ from datasets import load_dataset
52
+ import soundfile as sf
53
+
54
+ model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-covost2-de-en-st")
55
+ processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-covost2-de-en-st")
56
+
57
+ def map_to_array(batch):
58
+ speech, _ = sf.read(batch["file"])
59
+ batch["speech"] = speech
60
+ return batch
61
+
62
+ ds = load_dataset(
63
+ "patrickvonplaten/librispeech_asr_dummy",
64
+ "clean",
65
+ split="validation"
66
+ )
67
+ ds = ds.map(map_to_array)
68
+
69
+ inputs = processor(
70
+ ds["speech"][0],
71
+ sampling_rate=48_000,
72
+ return_tensors="pt"
73
+ )
74
+ generated_ids = model.generate(input_ids=inputs["input_features"], attention_mask=inputs["attention_mask"])
75
+
76
+ translation = processor.batch_decode(generated_ids, skip_special_tokens=True)
77
+ ```
78
+
79
+
80
+ ## Training data
81
+
82
+ The s2t-small-covost2-de-en-st is trained on German-English subset of [CoVoST2](https://github.com/facebookresearch/covost).
83
+ CoVoST is a large-scale multilingual ST corpus based on [Common Voice](https://arxiv.org/abs/1912.06670), created to to foster
84
+ ST research with the largest ever open dataset
85
+
86
+
87
+ ## Training procedure
88
+
89
+ ### Preprocessing
90
+
91
+ The speech data is pre-processed by extracting Kaldi-compliant 80-channel log mel-filter bank features automatically from
92
+ WAV/FLAC audio files via PyKaldi or torchaudio. Further utterance-level CMVN (cepstral mean and variance normalization)
93
+ is applied to each example.
94
+
95
+ The texts are lowercased and tokenized using character based SentencePiece vocab.
96
+
97
+
98
+ ### Training
99
+
100
+ The model is trained with standard autoregressive cross-entropy loss and using [SpecAugment](https://arxiv.org/abs/1904.08779).
101
+ The encoder receives speech features, and the decoder generates the transcripts autoregressively. To accelerate
102
+ model training and for better performance the encoder is pre-trained for English ASR.
103
+
104
+ ## Evaluation results
105
+
106
+ CoVOST2 test results for de-en (BLEU score): 17.58
107
+
108
+
109
+
110
+ ### BibTeX entry and citation info
111
+
112
+ ```bibtex
113
+ @inproceedings{wang2020fairseqs2t,
114
+ title = {fairseq S2T: Fast Speech-to-Text Modeling with fairseq},
115
+ author = {Changhan Wang and Yun Tang and Xutai Ma and Anne Wu and Dmytro Okhonko and Juan Pino},
116
+ booktitle = {Proceedings of the 2020 Conference of the Asian Chapter of the Association for Computational Linguistics (AACL): System Demonstrations},
117
+ year = {2020},
118
+ }
119
+
120
+ ```
config.json ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "activation_dropout": 0.1,
3
+ "activation_function": "relu",
4
+ "architectures": [
5
+ "Speech2TextForConditionalGeneration"
6
+ ],
7
+ "attention_dropout": 0.1,
8
+ "bos_token_id": 0,
9
+ "classifier_dropout": 0.0,
10
+ "conv_channels": 1024,
11
+ "conv_kernel_sizes": [
12
+ 5,
13
+ 5
14
+ ],
15
+ "d_model": 256,
16
+ "decoder_attention_heads": 4,
17
+ "decoder_ffn_dim": 2048,
18
+ "decoder_layerdrop": 0.0,
19
+ "decoder_layers": 6,
20
+ "decoder_start_token_id": 2,
21
+ "dropout": 0.1,
22
+ "early_stopping": true,
23
+ "encoder_attention_heads": 4,
24
+ "encoder_ffn_dim": 2048,
25
+ "encoder_layerdrop": 0.0,
26
+ "encoder_layers": 12,
27
+ "eos_token_id": 2,
28
+ "gradient_checkpointing": false,
29
+ "init_std": 0.02,
30
+ "input_channels": 1,
31
+ "input_feat_per_channel": 80,
32
+ "is_encoder_decoder": true,
33
+ "max_length": 200,
34
+ "max_source_positions": 6000,
35
+ "max_target_positions": 1024,
36
+ "model_type": "speech_to_text",
37
+ "num_beams": 5,
38
+ "num_conv_layers": 2,
39
+ "num_hidden_layers": 12,
40
+ "pad_token_id": 1,
41
+ "scale_embedding": true,
42
+ "tie_word_embeddings": false,
43
+ "transformers_version": "4.4.0.dev0",
44
+ "use_cache": true,
45
+ "vocab_size": 201
46
+ }
preprocessor_config.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "do_ceptral_normalize": true,
3
+ "feature_size": 80,
4
+ "normalize_means": true,
5
+ "normalize_vars": true,
6
+ "num_mel_bins": 80,
7
+ "padding_side": "right",
8
+ "padding_value": 0.0,
9
+ "return_attention_mask": true,
10
+ "sampling_rate": 48000
11
+ }
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d9c37134bd61e3dcf4ca533173f76e4ce90273760f338c5d0e6da2b99d09ec06
3
+ size 108439045
sentencepiece.bpe.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f3ecdf6958849f645b3335d2926180724ec693685e7d91082f5b2a905b834deb
3
+ size 239865
special_tokens_map.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
tokenizer_config.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "do_upper_case": false, "do_lower_case": false, "tgt_lang": null, "lang_codes": null, "tokenizer_file": null}
vocab.json ADDED
@@ -0,0 +1,203 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "<s>": 0,
3
+ "<pad>": 1,
4
+ "</s>": 2,
5
+ "<unk>": 3,
6
+ "\u2581": 4,
7
+ "e": 5,
8
+ "t": 6,
9
+ "a": 7,
10
+ "o": 8,
11
+ "i": 9,
12
+ "n": 10,
13
+ "s": 11,
14
+ "r": 12,
15
+ "h": 13,
16
+ "l": 14,
17
+ "d": 15,
18
+ "c": 16,
19
+ "u": 17,
20
+ "m": 18,
21
+ "f": 19,
22
+ ".": 20,
23
+ "p": 21,
24
+ "y": 22,
25
+ "g": 23,
26
+ "w": 24,
27
+ "b": 25,
28
+ "v": 26,
29
+ "k": 27,
30
+ "T": 28,
31
+ ",": 29,
32
+ "I": 30,
33
+ "A": 31,
34
+ "H": 32,
35
+ "S": 33,
36
+ "W": 34,
37
+ "x": 35,
38
+ "B": 36,
39
+ "?": 37,
40
+ "\u2019": 38,
41
+ "-": 39,
42
+ "M": 40,
43
+ "C": 41,
44
+ "z": 42,
45
+ "D": 43,
46
+ "F": 44,
47
+ "G": 45,
48
+ "P": 46,
49
+ "j": 47,
50
+ "E": 48,
51
+ "L": 49,
52
+ "O": 50,
53
+ "N": 51,
54
+ "q": 52,
55
+ "R": 53,
56
+ "!": 54,
57
+ "\"": 55,
58
+ "'": 56,
59
+ "Y": 57,
60
+ "K": 58,
61
+ "J": 59,
62
+ "\u201d": 60,
63
+ "\u201c": 61,
64
+ "U": 62,
65
+ "V": 63,
66
+ "\u00fc": 64,
67
+ "\u00f6": 65,
68
+ "Z": 66,
69
+ "\u2018": 67,
70
+ "\u00e4": 68,
71
+ ":": 69,
72
+ "\u00df": 70,
73
+ "Q": 71,
74
+ ";": 72,
75
+ "X": 73,
76
+ "\u00e1": 74,
77
+ "(": 75,
78
+ ")": 76,
79
+ "\u0301": 77,
80
+ "\u00f3": 78,
81
+ "\u00e9": 79,
82
+ "\u00ed": 80,
83
+ "/": 81,
84
+ "[": 82,
85
+ "]": 83,
86
+ "1": 84,
87
+ "\u2013": 85,
88
+ "0": 86,
89
+ "\u014d": 87,
90
+ "\u201e": 88,
91
+ "\u0161": 89,
92
+ "\u00d6": 90,
93
+ "\u0107": 91,
94
+ "\u00dc": 92,
95
+ "3": 93,
96
+ "\u0131": 94,
97
+ "\u0142": 95,
98
+ "7": 96,
99
+ "\u00e2": 97,
100
+ "\u0159": 98,
101
+ "\u00c9": 99,
102
+ "\u00e3": 100,
103
+ "4": 101,
104
+ "9": 102,
105
+ "\u00fd": 103,
106
+ "\u0101": 104,
107
+ "\u010d": 105,
108
+ "\u016b": 106,
109
+ "\u017e": 107,
110
+ "2": 108,
111
+ "6": 109,
112
+ "\u00eb": 110,
113
+ "\u00f8": 111,
114
+ "&": 112,
115
+ "\u00f4": 113,
116
+ "\u00fa": 114,
117
+ "\u00f1": 115,
118
+ "\u010c": 116,
119
+ "\u012b": 117,
120
+ "\u0160": 118,
121
+ "\u0219": 119,
122
+ "\u00c4": 120,
123
+ "\u02bf": 121,
124
+ "\u011b": 122,
125
+ "\u015f": 123,
126
+ "5": 124,
127
+ "8": 125,
128
+ "\u00c1": 126,
129
+ "\u0144": 127,
130
+ "\u014c": 128,
131
+ "\u00e7": 129,
132
+ "=": 130,
133
+ "\u00e0": 131,
134
+ "\u00e8": 132,
135
+ "\u015e": 133,
136
+ "\u017d": 134,
137
+ "\u021b": 135,
138
+ "\u2014": 136,
139
+ "\u00e5": 137,
140
+ "\u00ea": 138,
141
+ "\u00ee": 139,
142
+ "\u00f2": 140,
143
+ "\u0103": 141,
144
+ "\u0105": 142,
145
+ "\u0130": 143,
146
+ "\u015b": 144,
147
+ "\u0259": 145,
148
+ "%": 146,
149
+ "\u00ce": 147,
150
+ "\u00e6": 148,
151
+ "\u0110": 149,
152
+ "\u0141": 150,
153
+ "\u015a": 151,
154
+ "\u016f": 152,
155
+ "#": 153,
156
+ "`": 154,
157
+ "\u00ab": 155,
158
+ "\u00bb": 156,
159
+ "\u00d3": 157,
160
+ "\u00da": 158,
161
+ "\u00ef": 159,
162
+ "\u00fb": 160,
163
+ "\u011f": 161,
164
+ "\u0148": 162,
165
+ "\u0165": 163,
166
+ "\u1e2a": 164,
167
+ "\u201a": 165,
168
+ "\u2060": 166,
169
+ "$": 167,
170
+ "*": 168,
171
+ "+": 169,
172
+ "<": 170,
173
+ ">": 171,
174
+ "_": 172,
175
+ "\u00c2": 173,
176
+ "\u00c6": 174,
177
+ "\u00c7": 175,
178
+ "\u00d4": 176,
179
+ "\u00d8": 177,
180
+ "\u00f0": 178,
181
+ "\u00f5": 179,
182
+ "\u00f9": 180,
183
+ "\u010f": 181,
184
+ "\u0111": 182,
185
+ "\u0117": 183,
186
+ "\u0126": 184,
187
+ "\u012a": 185,
188
+ "\u0146": 186,
189
+ "\u0151": 187,
190
+ "\u0158": 188,
191
+ "\u017a": 189,
192
+ "\u017c": 190,
193
+ "\u03bc": 191,
194
+ "\u1e63": 192,
195
+ "\u1eaf": 193,
196
+ "\u2212": 194,
197
+ "\u2261": 195,
198
+ "\u30ab": 196,
199
+ "\u4e34": 197,
200
+ "\u5b59": 198,
201
+ "\u5c23": 199,
202
+ "\u9053": 200
203
+ }