Update README.md

README.md

@@ -12,7 +12,7 @@ tags:
 - xlsr-fine-tuning-week
 license: apache-2.0
 model-index:
-- name: XLSR Wav2Vec2 Chinese (zh-CN) by Jonatas Grosman
+- name: XLSR Wav2Vec2 Chinese (zh-CN) by wbbbbb
   results:
   - task: 
       name: Speech Recognition
@@ -24,17 +24,17 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 82.37
+         value: 70.47
        - name: Test CER
          type: cer
-         value: 19.03
+         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 thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :)
+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
 
@@ -46,55 +46,12 @@ Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library
 
 ```python
 from huggingsound import SpeechRecognitionModel
-model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn")
+model = SpeechRecognitionModel("wbbbbb/wav2vec2-large-chinese-zh-cn")
 audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
 transcriptions = model.transcribe(audio_paths)
 ```
 
-Writing your own inference script:
 
-```python
-import torch
-import librosa
-from datasets import load_dataset
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
-LANG_ID = "zh-CN"
-MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn"
-SAMPLES = 10
-test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
-processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
-model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
-# Preprocessing the datasets.
-# We need to read the audio files as arrays
-def speech_file_to_array_fn(batch):
-    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
-    batch["speech"] = speech_array
-    batch["sentence"] = batch["sentence"].upper()
-    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)
-with torch.no_grad():
-    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
-predicted_ids = torch.argmax(logits, dim=-1)
-predicted_sentences = processor.batch_decode(predicted_ids)
-for i, predicted_sentence in enumerate(predicted_sentences):
-    print("-" * 100)
-    print("Reference:", test_dataset[i]["sentence"])
-    print("Prediction:", predicted_sentence)
-```
-
-| Reference  | Prediction |
-| ------------- | ------------- |
-| 宋朝末年年间定居粉岭围。 | 宋朝末年年间定居分定为 |
-| 渐渐行动不便 | 建境行动不片 |
-| 二十一年去世。 | 二十一年去世 |
-| 他们自称恰哈拉。 | 他们自称家哈<unk> |
-| 局部干涩的例子包括有口干、眼睛干燥、及阴道干燥。 | 菊物干寺的例子包括有口肝眼睛干照以及阴到干<unk> |
-| 嘉靖三十八年，登进士第三甲第二名。 | 嘉靖三十八年登进士第三甲第二名 |
-| 这一名称一直沿用至今。 | 这一名称一直沿用是心 |
-| 同时乔凡尼还得到包税合同和许多明矾矿的经营权。 | 同时桥凡妮还得到包税合同和许多民繁矿的经营权 |
-| 为了惩罚西扎城和塞尔柱的结盟，盟军在抵达后将外城烧毁。 | 为了曾罚西扎城和塞尔素的节盟盟军在抵达后将外曾烧毁 |
-| 河内盛产黄色无鱼鳞的鳍射鱼。 | 合类生场环色无鱼林的骑射鱼 |
 
 ## Evaluation
 
@@ -106,21 +63,27 @@ 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 = "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn"
+MODEL_ID = "zh-CN-output-aishell"
 DEVICE = "cuda"
-CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
-                  "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
-                  "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
-                  "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
-                  "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "'", "ʻ", "ˆ"]
+
 test_dataset = load_dataset("common_voice", LANG_ID, split="test")
-wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
-cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
-chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
+
+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):
@@ -128,31 +91,55 @@ def speech_file_to_array_fn(batch):
         warnings.simplefilter("ignore")
         speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
     batch["speech"] = speech_array
-    batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
+    batch["sentence"] = (
+        re.sub("([^\u4e00-\u9fa5\u0030-\u0039])", "", batch["sentence"]).lower() + " "
+    )
     return batch
-test_dataset = test_dataset.map(speech_file_to_array_fn)
+
+
+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)
+    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
+        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.upper() for x in result["pred_strings"]]
-references = [x.upper() 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, chunk_size=1000) * 100}")
+
+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 2021-05-13). 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.
+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% |
 
@@ -164,7 +151,7 @@ If you want to cite this model you can use this:
 @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/jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn}},
+  howpublished={\url{https://huggingface.co/wbbbbb/wav2vec2-large-chinese-zh-cn}},
   year={2021}
 }
 ```