初始化项目，由ModelHub XC社区提供模型
Model: KBLab/wav2vec2-large-xlsr-53-swedish Source: Original Platform
This commit is contained in:
ModelHub XC
17
.gitattributes
vendored
Normal file
17
.gitattributes
vendored
Normal file
@@ -0,0 +1,17 @@
|
||||
*.bin.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.bin filter=lfs diff=lfs merge=lfs -text
|
||||
*.h5 filter=lfs diff=lfs merge=lfs -text
|
||||
*.tflite filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar.gz filter=lfs diff=lfs merge=lfs -text
|
||||
*.ot filter=lfs diff=lfs merge=lfs -text
|
||||
*.onnx filter=lfs diff=lfs merge=lfs -text
|
||||
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||
*.ftz filter=lfs diff=lfs merge=lfs -text
|
||||
*.joblib filter=lfs diff=lfs merge=lfs -text
|
||||
*.model filter=lfs diff=lfs merge=lfs -text
|
||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||
*.pb filter=lfs diff=lfs merge=lfs -text
|
||||
*.pt filter=lfs diff=lfs merge=lfs -text
|
||||
*.pth filter=lfs diff=lfs merge=lfs -text
|
||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||
136
README.md
Normal file
136
README.md
Normal file
@@ -0,0 +1,136 @@
|
||||
---
|
||||
language: sv
|
||||
datasets:
|
||||
- common_voice
|
||||
- KTH/nst
|
||||
metrics:
|
||||
- wer
|
||||
- cer
|
||||
tags:
|
||||
- audio
|
||||
- automatic-speech-recognition
|
||||
- speech
|
||||
- xlsr-fine-tuning-week
|
||||
license: apache-2.0
|
||||
model-index:
|
||||
- name: XLSR Wav2Vec2 Swedish by KBLab
|
||||
results:
|
||||
- task:
|
||||
name: Speech Recognition
|
||||
type: automatic-speech-recognition
|
||||
dataset:
|
||||
name: Common Voice sv-SE
|
||||
type: common_voice
|
||||
args: sv-SE
|
||||
metrics:
|
||||
- name: Test WER
|
||||
type: wer
|
||||
value: 14.298610
|
||||
- name: Test CER
|
||||
type: cer
|
||||
value: 4.925294
|
||||
---
|
||||
|
||||
# Wav2Vec2-Large-XLSR-53-Swedish
|
||||
|
||||
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [NST Swedish Dictation](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-17/).
|
||||
When using this model, make sure that your speech input is sampled at 16kHz.
|
||||
|
||||
**Note:** We recommend using our newer model [wav2vec2-large-voxrex-swedish](https://huggingface.co/KBLab/wav2vec2-large-voxrex-swedish) for the best performance.
|
||||
|
||||
## Usage
|
||||
|
||||
The model can be used directly (without a language model) as follows:
|
||||
|
||||
```python
|
||||
import torch
|
||||
import torchaudio
|
||||
from datasets import load_dataset
|
||||
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
|
||||
|
||||
test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]").
|
||||
|
||||
processor = Wav2Vec2Processor.from_pretrained("KBLab/wav2vec2-large-xlsr-53-swedish")
|
||||
model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-large-xlsr-53-swedish")
|
||||
|
||||
resampler = torchaudio.transforms.Resample(48_000, 16_000)
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# We need to read the aduio files as arrays
|
||||
def speech_file_to_array_fn(batch):
|
||||
speech_array, sampling_rate = torchaudio.load(batch["path"])
|
||||
batch["speech"] = resampler(speech_array).squeeze().numpy()
|
||||
|
||||
return batch
|
||||
|
||||
test_dataset = test_dataset.map(speech_file_to_array_fn)
|
||||
inputs = processor(test_dataset["speech"][:2], 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)
|
||||
|
||||
print("Prediction:", processor.batch_decode(predicted_ids))
|
||||
print("Reference:", test_dataset["sentence"][:2])
|
||||
```
|
||||
|
||||
|
||||
## Evaluation
|
||||
|
||||
The model can be evaluated as follows on the Swedish test data of Common Voice.
|
||||
|
||||
|
||||
```python
|
||||
import torch
|
||||
import torchaudio
|
||||
from datasets import load_dataset, load_metric
|
||||
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
|
||||
import re
|
||||
|
||||
test_dataset = load_dataset("common_voice", "sv-SE", split="test")
|
||||
wer = load_metric("wer")
|
||||
|
||||
processor = Wav2Vec2Processor.from_pretrained("KBLab/wav2vec2-large-xlsr-53-swedish")
|
||||
model = Wav2Vec2ForCTC.from_pretrained("KBLab/wav2vec2-large-xlsr-53-swedish")
|
||||
model.to("cuda")
|
||||
|
||||
chars_to_ignore_regex = '[,?.!\\-;:"“]'
|
||||
resampler = torchaudio.transforms.Resample(48_000, 16_000)
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# We need to read the aduio files as arrays
|
||||
def speech_file_to_array_fn(batch):
|
||||
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
|
||||
speech_array, sampling_rate = torchaudio.load(batch["path"])
|
||||
batch["speech"] = resampler(speech_array).squeeze().numpy()
|
||||
|
||||
return batch
|
||||
|
||||
test_dataset = test_dataset.map(speech_file_to_array_fn)
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# We need to read the aduio 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("cuda"), attention_mask=inputs.attention_mask.to("cuda")).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)
|
||||
|
||||
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
|
||||
print("CER: {:2f}".format(100 * wer.compute(predictions=[" ".join(list(entry)) for entry in result["pred_strings"]], references=[" ".join(list(entry)) for entry in result["sentence"]])))
|
||||
```
|
||||
|
||||
**WER**: 14.298610%
|
||||
**CER**: 4.925294%
|
||||
|
||||
## Training
|
||||
|
||||
First the XLSR model was further pre-trained for 50 epochs with a corpus consisting of 1000 hours spoken Swedish from various radio stations. Secondly [NST Swedish Dictation](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-17/) was used for fine tuning as well as [Common Voice](https://commonvoice.mozilla.org/en/datasets). Lastly only Common Voice dataset was used for final finetuning. The [Fairseq](https://github.com/fairseq) scripts were used.
|
||||
68
config.json
Normal file
68
config.json
Normal file
@@ -0,0 +1,68 @@
|
||||
{
|
||||
"activation_dropout": 0.1,
|
||||
"apply_spec_augment": true,
|
||||
"architectures": [
|
||||
"Wav2Vec2ForCTC"
|
||||
],
|
||||
"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": "4.4.0.dev0",
|
||||
"vocab_size": 46
|
||||
}
|
||||
3
flax_model.msgpack
Normal file
3
flax_model.msgpack
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ccb34bc1a20832a02087d80e50174c2b0c8aba25bc4771fc7b7285616a59d4ed
|
||||
size 1261958872
|
||||
8
preprocessor_config.json
Normal file
8
preprocessor_config.json
Normal file
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"do_normalize": true,
|
||||
"feature_size": 1,
|
||||
"padding_side": "right",
|
||||
"padding_value": 0.0,
|
||||
"return_attention_mask": true,
|
||||
"sampling_rate": 16000
|
||||
}
|
||||
3
pytorch_model.bin
Normal file
3
pytorch_model.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:64da056c24b86761464f7ba8e51325a7578778846e24c0486959a0c7f717ee4b
|
||||
size 1262116567
|
||||
2
special_tokens_map.json
Normal file
2
special_tokens_map.json
Normal file
@@ -0,0 +1,2 @@
|
||||
{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
|
||||
|
||||
1
tokenizer_config.json
Normal file
1
tokenizer_config.json
Normal file
@@ -0,0 +1 @@
|
||||
{"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "do_lower_case": true, "return_attention_mask": false, "do_normalize": true}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
@@ -0,0 +1 @@
|
||||
{"<pad>": 0, "<s>": 1, "</s>": 2, "<unk>": 3, "|": 4, "T": 5, "E": 6, "A": 7, "N": 8, "R": 9, "S": 10, "I": 11, "L": 12, "D": 13, "O": 14, "M": 15, "K": 16, "G": 17, "U": 18, "V": 19, "F": 20, "H": 21, "\u00c4": 22, "\u00c5": 23, "P": 24, "\u00d6": 25, "B": 26, "J": 27, "C": 28, "Y": 29, "X": 30, "W": 31, "Z": 32, "\u00c9": 33, "Q": 34, "8": 35, "7": 36, "6": 37, "5": 38, "3": 39, "2": 40, "4": 41, "9": 42, "1": 43, "0": 44, "'": 45}
|
||||
Reference in New Issue
Block a user