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Model: wbbbbb/wav2vec2-large-chinese-zh-cn
Source: Original Platform

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README.md

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+---
+language: zh
+datasets:
+- common_voice
+metrics:
+- wer
+- cer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: XLSR Wav2Vec2 Chinese (zh-CN) by wbbbbb
+  results:
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice zh-CN
+      type: common_voice
+      args: zh-CN
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 70.47
+       - name: Test CER
+         type: cer
+         value: 12.30
+---
+# Fine-tuned XLSR-53 large model for speech recognition in Chinese
+
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Chinese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyong/css10) and [ST-CMDS](http://www.openslr.org/38/).
+When using this model, make sure that your speech input is sampled at 16kHz.
+
+This model has been fine-tuned on RTX3090 for 50h
+
+The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
+
+## Usage
+
+The model can be used directly (without a language model) as follows...
+
+Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
+
+```python
+from huggingsound import SpeechRecognitionModel
+model = SpeechRecognitionModel("wbbbbb/wav2vec2-large-chinese-zh-cn")
+audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
+transcriptions = model.transcribe(audio_paths)
+```
+
+
+
+## Evaluation
+
+The model can be evaluated as follows on the Chinese (zh-CN) test data of Common Voice.
+
+```python
+import torch
+import re
+import librosa
+from datasets import load_dataset, load_metric
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import warnings
+import os
+
+os.environ["KMP_AFFINITY"] = ""
+
+
+LANG_ID = "zh-CN"
+MODEL_ID = "zh-CN-output-aishell"
+DEVICE = "cuda"
+
+test_dataset = load_dataset("common_voice", LANG_ID, split="test")
+
+wer = load_metric("wer")
+cer = load_metric("cer")
+
+
+
+processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+model.to(DEVICE)
+
+# Preprocessing the datasets.
+# We need to read the audio files as arrays
+def speech_file_to_array_fn(batch):
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+        speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
+    batch["speech"] = speech_array
+    batch["sentence"] = (
+        re.sub("([^\u4e00-\u9fa5\u0030-\u0039])", "", batch["sentence"]).lower() + " "
+    )
+    return batch
+
+
+test_dataset = test_dataset.map(
+    speech_file_to_array_fn,
+    num_proc=15,
+    remove_columns=['client_id', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],
+)
+
+# Preprocessing the datasets.
+# We need to read the audio 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(DEVICE),
+            attention_mask=inputs.attention_mask.to(DEVICE),
+        ).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)
+
+predictions = [x.lower() for x in result["pred_strings"]]
+references = [x.lower() for x in result["sentence"]]
+
+print(
+    f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}"
+)
+print(f"CER: {cer.compute(predictions=predictions, references=references) * 100}")
+
+```
+
+**Test Result**:
+
+In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2022-07-18). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.
+
+| Model | WER | CER |
+| ------------- | ------------- | ------------- |
+| wbbbbb/wav2vec2-large-chinese-zh-cn | **70.47%** | **12.30%** |
+| jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn | **82.37%** | **19.03%** |
+| ydshieh/wav2vec2-large-xlsr-53-chinese-zh-cn-gpt | 84.01% | 20.95% |
+
+
+## Citation
+If you want to cite this model you can use this:
+
+```bibtex
+@misc{grosman2021xlsr53-large-chinese,
+  title={Fine-tuned {XLSR}-53 large model for speech recognition in {C}hinese},
+  author={Grosman, Jonatas},
+  howpublished={\url{https://huggingface.co/wbbbbb/wav2vec2-large-chinese-zh-cn}},
+  year={2021}
+}
+```

config.json

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+  "_name_or_path": "TencentGameMate/chinese-wav2vec2-large",
+  "activation_dropout": 0.0,
+  "adapter_kernel_size": 3,
+  "adapter_stride": 2,
+  "add_adapter": false,
+  "apply_spec_augment": true,
+  "architectures": [
+    "Wav2Vec2ForPreTraining"
+  ],
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+  "do_stable_layer_norm": true,
+  "eos_token_id": 2,
+  "feat_extract_activation": "gelu",
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+  "feat_quantizer_dropout": 0.0,
+  "final_dropout": 0.0,
+  "hidden_act": "gelu",
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+  "layer_norm_eps": 1e-05,
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+  "mask_time_selection": "static",
+  "model_type": "wav2vec2",
+  "num_adapter_layers": 3,
+  "num_attention_heads": 16,
+  "num_codevector_groups": 2,
+  "num_codevectors_per_group": 320,
+  "num_conv_pos_embedding_groups": 16,
+  "num_conv_pos_embeddings": 128,
+  "num_feat_extract_layers": 7,
+  "num_hidden_layers": 24,
+  "num_negatives": 100,
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+  "pad_token_id": 5168,
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+  "transformers_version": "4.21.0.dev0",
+  "use_weighted_layer_sum": false,
+  "vocab_size": 5171,
+  "xvector_output_dim": 512
+}

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