.gitattributes

@@ -14,4 +14,3 @@
 *.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

README.md

@@ -1,218 +0,0 @@
----
-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
-```

config.json

@@ -1,76 +0,0 @@
-{
-  "_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
-}

preprocessor_config.json

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

special_tokens_map.json

@@ -1 +0,0 @@
-{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "[UNK]", "pad_token": "[PAD]"}

tokenizer_config.json

@@ -1 +0,0 @@
-{"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|"}

vocab.json

@@ -1 +0,0 @@
-{"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}