初始化项目，由ModelHub XC社区提供模型
Model: maxidl/wav2vec2-large-xlsr-german 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
|
||||
218
README.md
Normal file
218
README.md
Normal file
@@ -0,0 +1,218 @@
|
||||
---
|
||||
language: de
|
||||
datasets:
|
||||
- common_voice
|
||||
metrics:
|
||||
- wer
|
||||
tags:
|
||||
- audio
|
||||
- automatic-speech-recognition
|
||||
- speech
|
||||
- xlsr-fine-tuning-week
|
||||
license: apache-2.0
|
||||
model-index:
|
||||
- name: XLSR Wav2Vec2 Large 53 CV-de
|
||||
results:
|
||||
- task:
|
||||
name: Speech Recognition
|
||||
type: automatic-speech-recognition
|
||||
dataset:
|
||||
name: Common Voice de
|
||||
type: common_voice
|
||||
args: de
|
||||
metrics:
|
||||
- name: Test WER
|
||||
type: wer
|
||||
value: 12.77
|
||||
---
|
||||
|
||||
# Wav2Vec2-Large-XLSR-53-German
|
||||
|
||||
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on German using the [Common Voice](https://huggingface.co/datasets/common_voice) dataset.
|
||||
When using this model, make sure that your speech input is sampled at 16kHz.
|
||||
|
||||
## 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", "de", split="test[:8]") # use a batch of 8 for demo purposes
|
||||
|
||||
processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
|
||||
model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
|
||||
|
||||
resampler = torchaudio.transforms.Resample(48_000, 16_000)
|
||||
|
||||
"""
|
||||
Preprocessing the dataset by:
|
||||
- loading audio files
|
||||
- resampling to 16kHz
|
||||
- converting to array
|
||||
- prepare input tensor using the processor
|
||||
"""
|
||||
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"], sampling_rate=16_000, return_tensors="pt", padding=True)
|
||||
|
||||
# run forward
|
||||
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"])
|
||||
"""
|
||||
Example Result:
|
||||
|
||||
Prediction: [
|
||||
'zieh durch bittet draußen die schuhe aus',
|
||||
'es kommt zugvorgebauten fo',
|
||||
'ihre vorterstrecken erschienen it modemagazinen wie der voge karpes basar mariclair',
|
||||
'fürliepert eine auch für manachen ungewöhnlich lange drittelliste',
|
||||
'er wurde zu ehren des reichskanzlers otto von bismarck errichtet',
|
||||
'was solls ich bin bereit',
|
||||
'das internet besteht aus vielen computern die miteinander verbunden sind',
|
||||
'der uranus ist der siebinteplanet in unserem sonnensystem s'
|
||||
]
|
||||
|
||||
Reference: [
|
||||
'Zieht euch bitte draußen die Schuhe aus.',
|
||||
'Es kommt zum Showdown in Gstaad.',
|
||||
'Ihre Fotostrecken erschienen in Modemagazinen wie der Vogue, Harper’s Bazaar und Marie Claire.',
|
||||
'Felipe hat eine auch für Monarchen ungewöhnlich lange Titelliste.',
|
||||
'Er wurde zu Ehren des Reichskanzlers Otto von Bismarck errichtet.',
|
||||
'Was solls, ich bin bereit.',
|
||||
'Das Internet besteht aus vielen Computern, die miteinander verbunden sind.',
|
||||
'Der Uranus ist der siebente Planet in unserem Sonnensystem.'
|
||||
]
|
||||
"""
|
||||
```
|
||||
|
||||
|
||||
## Evaluation
|
||||
|
||||
The model can be evaluated as follows on the German test data of Common Voice:
|
||||
|
||||
|
||||
```python
|
||||
import re
|
||||
import torch
|
||||
import torchaudio
|
||||
from datasets import load_dataset, load_metric
|
||||
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
|
||||
|
||||
"""
|
||||
Evaluation on the full test set:
|
||||
- takes ~20mins (RTX 3090).
|
||||
- requires ~170GB RAM to compute the WER. Below, we use a chunked implementation of WER to avoid large RAM consumption.
|
||||
"""
|
||||
test_dataset = load_dataset("common_voice", "de", split="test") # use "test[:1%]" for 1% sample
|
||||
wer = load_metric("wer")
|
||||
|
||||
processor = Wav2Vec2Processor.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
|
||||
model = Wav2Vec2ForCTC.from_pretrained("maxidl/wav2vec2-large-xlsr-german")
|
||||
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):
|
||||
\tbatch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
|
||||
\tspeech_array, sampling_rate = torchaudio.load(batch["path"])
|
||||
\tbatch["speech"] = resampler(speech_array).squeeze().numpy()
|
||||
\treturn batch
|
||||
|
||||
test_dataset = test_dataset.map(speech_file_to_array_fn)
|
||||
|
||||
# Preprocessing the datasets.
|
||||
# We need to read the audio files as arrays
|
||||
def evaluate(batch):
|
||||
\tinputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
|
||||
|
||||
\twith torch.no_grad():
|
||||
\t\tlogits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
|
||||
|
||||
\tpred_ids = torch.argmax(logits, dim=-1)
|
||||
\tbatch["pred_strings"] = processor.batch_decode(pred_ids)
|
||||
\treturn batch
|
||||
|
||||
result = test_dataset.map(evaluate, batched=True, batch_size=8) # batch_size=8 -> requires ~14.5GB GPU memory
|
||||
|
||||
# non-chunked version:
|
||||
# print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
|
||||
# WER: 12.900291
|
||||
|
||||
# Chunked version, see https://discuss.huggingface.co/t/spanish-asr-fine-tuning-wav2vec2/4586/5:
|
||||
import jiwer
|
||||
|
||||
def chunked_wer(targets, predictions, chunk_size=None):
|
||||
if chunk_size is None: return jiwer.wer(targets, predictions)
|
||||
start = 0
|
||||
end = chunk_size
|
||||
H, S, D, I = 0, 0, 0, 0
|
||||
while start < len(targets):
|
||||
chunk_metrics = jiwer.compute_measures(targets[start:end], predictions[start:end])
|
||||
H = H + chunk_metrics["hits"]
|
||||
S = S + chunk_metrics["substitutions"]
|
||||
D = D + chunk_metrics["deletions"]
|
||||
I = I + chunk_metrics["insertions"]
|
||||
start += chunk_size
|
||||
end += chunk_size
|
||||
return float(S + D + I) / float(H + S + D)
|
||||
|
||||
print("Total (chunk_size=1000), WER: {:2f}".format(100 * chunked_wer(result["pred_strings"], result["sentence"], chunk_size=1000)))
|
||||
# Total (chunk=1000), WER: 12.768981
|
||||
```
|
||||
|
||||
**Test Result**: WER: 12.77 %
|
||||
|
||||
|
||||
## Training
|
||||
|
||||
The Common Voice German `train` and `validation` were used for training.
|
||||
The script used for training can be found [here](https://github.com/maxidl/wav2vec2).
|
||||
The model was trained for 50k steps, taking around 30 hours on a single A100.
|
||||
|
||||
The arguments used for training this model are:
|
||||
```
|
||||
python run_finetuning.py \\
|
||||
--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \\
|
||||
--dataset_config_name="de" \\
|
||||
--output_dir=./wav2vec2-large-xlsr-german \\
|
||||
--preprocessing_num_workers="16" \\
|
||||
--overwrite_output_dir \\
|
||||
--num_train_epochs="20" \\
|
||||
--per_device_train_batch_size="64" \\
|
||||
--per_device_eval_batch_size="32" \\
|
||||
--learning_rate="1e-4" \\
|
||||
--warmup_steps="500" \\
|
||||
--evaluation_strategy="steps" \\
|
||||
--save_steps="5000" \\
|
||||
--eval_steps="5000" \\
|
||||
--logging_steps="1000" \\
|
||||
--save_total_limit="3" \\
|
||||
--freeze_feature_extractor \\
|
||||
--activation_dropout="0.055" \\
|
||||
--attention_dropout="0.094" \\
|
||||
--feat_proj_dropout="0.04" \\
|
||||
--layerdrop="0.04" \\
|
||||
--mask_time_prob="0.08" \\
|
||||
--gradient_checkpointing="1" \\
|
||||
--fp16 \\
|
||||
--do_train \\
|
||||
--do_eval \\
|
||||
--dataloader_num_workers="16" \\
|
||||
--group_by_length
|
||||
```
|
||||
76
config.json
Normal file
76
config.json
Normal file
@@ -0,0 +1,76 @@
|
||||
{
|
||||
"_name_or_path": "facebook/wav2vec2-large-xlsr-53",
|
||||
"activation_dropout": 0.055,
|
||||
"apply_spec_augment": true,
|
||||
"architectures": [
|
||||
"Wav2Vec2ForCTC"
|
||||
],
|
||||
"attention_dropout": 0.094,
|
||||
"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": "mean",
|
||||
"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": 0.04,
|
||||
"final_dropout": 0.0,
|
||||
"gradient_checkpointing": true,
|
||||
"hidden_act": "gelu",
|
||||
"hidden_dropout": 0.1,
|
||||
"hidden_size": 1024,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 4096,
|
||||
"layer_norm_eps": 1e-05,
|
||||
"layerdrop": 0.04,
|
||||
"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": 0.08,
|
||||
"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": 171,
|
||||
"transformers_version": "4.4.2",
|
||||
"vocab_size": 174
|
||||
}
|
||||
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 sha256:1a40ff8d1041dc9fbc585147ae94303cead8ca5116930a2b910e318aa6906c07
|
||||
size 1262483676
|
||||
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 sha256:20e7c1b2537b683c9b9997816726695bd05f61c4f92f379ddf3d833d77b4ea1b
|
||||
size 1262647255
|
||||
1
special_tokens_map.json
Normal file
1
special_tokens_map.json
Normal file
@@ -0,0 +1 @@
|
||||
{"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": false, "word_delimiter_token": "|"}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
@@ -0,0 +1 @@
|
||||
{"к": 0, "„": 1, "⟩": 2, "ą": 3, "‟": 4, "о": 5, "臣": 6, "å": 7, "_": 8, "ğ": 9, "ï": 10, "x": 11, "ź": 12, "ç": 13, "ö": 14, "´": 15, "ë": 16, "ä": 17, "t": 18, "f": 19, "ň": 20, "ť": 21, "ņ": 22, "ü": 23, "s": 24, "е": 25, "ả": 26, "b": 27, "в": 28, "ū": 29, "q": 30, "ț": 31, "̆": 32, "≡": 33, "̇": 34, "ś": 35, "孙": 36, "ṟ": 38, "i": 39, "û": 40, "ō": 41, "`": 42, "z": 43, "‑": 44, "ན": 45, "ù": 46, "r": 47, "и": 48, "″": 49, "ọ": 50, "e": 51, "w": 52, "ạ": 53, "a": 54, "比": 55, "ʻ": 56, "°": 57, "d": 58, "ī": 59, "g": 60, "k": 61, "カ": 62, "ч": 63, "=": 64, "ż": 65, "ħ": 66, "o": 67, "ñ": 68, "ô": 69, "ď": 70, "ø": 71, "ž": 72, "y": 73, "ý": 74, "ș": 75, "ḫ": 76, "ó": 77, "ē": 78, "ő": 79, "無": 80, "à": 81, "ş": 82, "→": 83, "ộ": 84, "с": 85, "ắ": 86, "ê": 87, "”": 88, "ă": 89, "幺": 90, "é": 91, "h": 92, "′": 93, "ř": 94, "]": 95, "尣": 96, "乡": 97, "á": 98, "æ": 99, "ǐ": 100, "ı": 101, "[": 102, "î": 103, "u": 104, "’": 105, "č": 106, "⟨": 107, "p": 108, "ę": 109, "l": 110, "ā": 111, "ằ": 112, "c": 113, "š": 114, "“": 115, "临": 116, "ѹ": 117, "ć": 118, "ṭ": 119, "ŏ": 120, "m": 121, "ě": 122, "¡": 123, "ů": 124, "‹": 125, "…": 126, "道": 127, "་": 128, "м": 129, "v": 130, "ń": 131, "р": 132, "ф": 133, "‘": 134, "›": 135, "»": 136, "ð": 137, "«": 138, "支": 139, "þ": 140, "ế": 141, "ễ": 142, "ʿ": 143, "ġ": 144, "í": 145, "ß": 146, "–": 147, "ú": 148, "ứ": 149, "ə": 150, "n": 151, "а": 152, "ш": 153, "đ": 154, "‚": 155, "ã": 156, "â": 157, "œ": 158, "−": 159, "µ": 160, "—": 161, "ṣ": 162, "õ": 163, "ò": 164, "辶": 165, "j": 166, "ì": 167, "ė": 168, "ł": 169, "|": 37, "[UNK]": 170, "[PAD]": 171}
|
||||
Reference in New Issue
Block a user