README.md
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1 ---
2 language: fa
3 datasets:
4 - common_voice
5 metrics:
6 - wer
7 - cer
8 tags:
9 - audio
10 - automatic-speech-recognition
11 - speech
12 - xlsr-fine-tuning-week
13 license: apache-2.0
14 model-index:
15 - name: XLSR Wav2Vec2 Persian by Jonatas Grosman
16 results:
17 - task:
18 name: Speech Recognition
19 type: automatic-speech-recognition
20 dataset:
21 name: Common Voice fa
22 type: common_voice
23 args: fa
24 metrics:
25 - name: Test WER
26 type: wer
27 value: 30.12
28 - name: Test CER
29 type: cer
30 value: 7.37
31 ---
32
33 # Fine-tuned XLSR-53 large model for speech recognition in Persian
34
35 Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Persian using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice).
36 When using this model, make sure that your speech input is sampled at 16kHz.
37
38 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/) :)
39
40 The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
41
42 ## Usage
43
44 The model can be used directly (without a language model) as follows...
45
46 Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
47
48 ```python
49 from huggingsound import SpeechRecognitionModel
50
51 model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-persian")
52 audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
53
54 transcriptions = model.transcribe(audio_paths)
55 ```
56
57 Writing your own inference script:
58
59 ```python
60 import torch
61 import librosa
62 from datasets import load_dataset
63 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
64
65 LANG_ID = "fa"
66 MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-persian"
67 SAMPLES = 5
68
69 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
70
71 processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
72 model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
73
74 # Preprocessing the datasets.
75 # We need to read the audio files as arrays
76 def speech_file_to_array_fn(batch):
77 speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
78 batch["speech"] = speech_array
79 batch["sentence"] = batch["sentence"].upper()
80 return batch
81
82 test_dataset = test_dataset.map(speech_file_to_array_fn)
83 inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
84
85 with torch.no_grad():
86 logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
87
88 predicted_ids = torch.argmax(logits, dim=-1)
89 predicted_sentences = processor.batch_decode(predicted_ids)
90
91 for i, predicted_sentence in enumerate(predicted_sentences):
92 print("-" * 100)
93 print("Reference:", test_dataset[i]["sentence"])
94 print("Prediction:", predicted_sentence)
95 ```
96
97 | Reference | Prediction |
98 | ------------- | ------------- |
99 | از مهمونداری کنار بکشم | از مهمانداری کنار بکشم |
100 | برو از مهرداد بپرس. | برو از ماقدعاد به پرس |
101 | خب ، تو چیكار می كنی؟ | خوب تو چیکار می کنی |
102 | مسقط پایتخت عمان در عربی به معنای محل سقوط است | مسقط پایتخت عمان در عربی به بعنای محل سقوط است |
103 | آه، نه اصلاُ! | اهنه اصلا |
104 | توانست | توانست |
105 | قصیده فن شعر میگوید ای دوستان | قصیده فن شعر میگوید ایدوستون |
106 | دو استایل متفاوت دارین | دوبوست داریل و متفاوت بری |
107 | دو روز قبل از کریسمس ؟ | اون مفتود پش پشش |
108 | ساعت های کاری چیست؟ | این توری که موشیکل خب |
109
110 ## Evaluation
111
112 The model can be evaluated as follows on the Persian test data of Common Voice.
113
114 ```python
115 import torch
116 import re
117 import librosa
118 from datasets import load_dataset, load_metric
119 from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
120
121 LANG_ID = "fa"
122 MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-persian"
123 DEVICE = "cuda"
124
125 CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞",
126 "؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]",
127 "{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。",
128 "、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽",
129 "『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"]
130
131 test_dataset = load_dataset("common_voice", LANG_ID, split="test")
132
133 wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
134 cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
135
136 chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
137
138 processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
139 model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
140 model.to(DEVICE)
141
142 # Preprocessing the datasets.
143 # We need to read the audio files as arrays
144 def speech_file_to_array_fn(batch):
145 with warnings.catch_warnings():
146 warnings.simplefilter("ignore")
147 speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
148 batch["speech"] = speech_array
149 batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper()
150 return batch
151
152 test_dataset = test_dataset.map(speech_file_to_array_fn)
153
154 # Preprocessing the datasets.
155 # We need to read the audio files as arrays
156 def evaluate(batch):
157 inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
158
159 with torch.no_grad():
160 logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits
161
162 pred_ids = torch.argmax(logits, dim=-1)
163 batch["pred_strings"] = processor.batch_decode(pred_ids)
164 return batch
165
166 result = test_dataset.map(evaluate, batched=True, batch_size=8)
167
168 predictions = [x.upper() for x in result["pred_strings"]]
169 references = [x.upper() for x in result["sentence"]]
170
171 print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
172 print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
173 ```
174
175 **Test Result**:
176
177 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 2021-04-22). 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.
178
179 | Model | WER | CER |
180 | ------------- | ------------- | ------------- |
181 | jonatasgrosman/wav2vec2-large-xlsr-53-persian | **30.12%** | **7.37%** |
182 | m3hrdadfi/wav2vec2-large-xlsr-persian-v2 | 33.85% | 8.79% |
183 | m3hrdadfi/wav2vec2-large-xlsr-persian | 34.37% | 8.98% |
184
185 ## Citation
186 If you want to cite this model you can use this:
187
188 ```bibtex
189 @misc{grosman2021xlsr53-large-persian,
190 title={Fine-tuned {XLSR}-53 large model for speech recognition in {P}ersian},
191 author={Grosman, Jonatas},
192 howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-persian}},
193 year={2021}
194 }
195 ```