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--- |
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language: |
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- en |
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- zh |
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license: cc-by-sa-4.0 |
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size_categories: |
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- 10K<n<100K |
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dataset_info: |
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- config_name: default |
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features: |
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- name: id |
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dtype: string |
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- name: path |
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dtype: string |
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- name: audio |
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dtype: |
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audio: |
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sampling_rate: 16000 |
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- name: transcription |
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dtype: string |
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- name: duration |
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dtype: float32 |
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- name: language |
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dtype: string |
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- name: original_speaker_id |
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dtype: int64 |
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- name: session_id |
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dtype: int64 |
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- name: topic |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 1014558975.36 |
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num_examples: 9869 |
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- name: test |
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num_bytes: 106170264.135 |
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num_examples: 1315 |
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- name: validation |
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num_bytes: 106771606.91 |
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num_examples: 1130 |
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download_size: 1223500329 |
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dataset_size: 1227500846.4050002 |
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configs: |
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- config_name: default |
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data_files: |
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- path: data/train-* |
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split: train |
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- path: data/test-* |
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split: test |
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- path: data/validation-* |
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split: validation |
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- config_name: 30s |
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data_files: |
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- path: 30s/train-* |
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split: train |
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- path: 30s/validation-* |
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split: validation |
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- path: 30s/test-* |
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split: test |
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--- |
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# Dataset Card for Dataset Name |
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This dataset is derived from CAiRE/ASCEND. More information is available at https://huggingface.co/datasets/CAiRE/ASCEND. |
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- Removed 嗯 呃 um uh |
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- Resolved [UNK]'s using whisper-medium |
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## Usage |
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- Default utterances with cleaned transcripts |
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```python |
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from datasets import load_dataset |
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data = load_dataset("georgechang8/ASCEND_CLEAN") # add split="train" for train set, etc. |
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``` |
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- Concatenated 30s utterances with cleaned transcripts |
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- https://github.com/George0828Zhang/distil-whisper/blob/main/training/run_concatenate.py |
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```python |
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data = load_dataset("georgechang8/ASCEND_CLEAN", "30s") # add split="train" for train set, etc. |
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``` |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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- **Language(s):** English, Simplified Chinese, Mixed |
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- **License:** Creative Common Attribution Share-Alike 4.0 International (CC-BY-SA 4.0) |
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## Dataset Creation |
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### Source Data |
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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https://huggingface.co/datasets/CAiRE/ASCEND |
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#### Data Collection and Processing |
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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1. Load from source |
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```python |
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from datasets import load_dataset, Audio as DSAudio |
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data_raw = load_dataset("CAiRE/ASCEND") |
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data_raw = data_raw.cast_column("audio", DSAudio(sampling_rate=16000)) |
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``` |
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2. Clean stop words |
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```python |
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import re |
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def clean_transcripts(x): |
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cjk = "[\u3400-\u4db5\u4e00-\u9fa5\u9fa6-\u9fbb\uf900-\ufa2d\ufa30-\ufa6a\ufa70-\ufad9\uff00-\uffef\u2e80-\u2eff\u3000-\u303f\u31c0-\u31ef\u2f00-\u2fdf\u2ff0-\u2fff\u3100-\u312f\u31a0-\u31bf\ufe10-\ufe1f\ufe30-\ufe4f\u2600-\u26ff\u2700-\u27bf\u3200-\u32ff\u3300-\u33ff]" |
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x = re.sub(r'\.\.\.|\s|^|$', ' ', x) # expanding space allows matching " uh uh" case |
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x = re.sub(rf"({cjk}|\s)([Uu][mh]|U[MH])({cjk}|\s)", r"\1 \3", x) # replace any uh surrounded by cjk or space |
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x = x.replace('嗯', ' ') |
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x = x.replace('呃', ' ') |
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x = re.sub(r"\s+", " ", x) |
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return x.strip() |
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data = data_raw.map(lambda x: {"transcription": clean_transcripts(x['transcription'])}) |
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data = data.filter(lambda x: x["transcription"] != "") |
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``` |
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3. Isolate samples with UNKs |
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```python |
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unks = data.filter(lambda x: "[UNK]" in x["transcription"]) |
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unks.shape |
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``` |
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> {'train': (402, 9), 'test': (36, 9), 'validation': (63, 9)} |
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4. Load whisper model. For Chinese, medium performs best. |
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```python |
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from stable_whisper import load_faster_whisper |
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model = load_faster_whisper( |
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"medium", |
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device="cuda", |
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compute_type="float16", |
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) |
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``` |
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5. Resolve UNKs with whisper-medium |
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```python |
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from sacrebleu.tokenizers.tokenizer_zh import TokenizerZh |
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from whisper_normalizer.basic import BasicTextNormalizer |
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import cn2an |
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import json |
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import jiwer |
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from tqdm.auto import tqdm |
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sacretok = TokenizerZh() |
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whisper_norm = BasicTextNormalizer() |
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def compute_mer(hyp, ref): |
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def norm(x): |
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return sacretok(cn2an.transform(whisper_norm(x), "an2cn")) |
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return jiwer.process_words(norm(hyp), norm(ref)).wer * 100 |
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adjusted = {split:dict() for split in data} |
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double_check = {split:dict() for split in data} |
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UNK = "[UNK]" |
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for split in data: |
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trange = tqdm(unks[split], desc=split) |
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for i,sample in enumerate(trange): |
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transcription = sample['transcription'] |
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texts = transcription.split(UNK) |
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words = [] |
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for sent in texts[1:]: |
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for w in sacretok(sent).split(): |
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if w not in words: |
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words += [w] |
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keyword = "关键词" |
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header = "字幕" |
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prompt = f"{keyword} \"{'/'.join(words)}\" {header} " |
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result = model.transcribe_stable( |
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audio=sample['audio']['array'], |
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initial_prompt=prompt, # encourage reuse of words |
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prefix=texts[0], # forcing start to follow real start |
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language=sample['language'].replace('mixed', 'zh'), |
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regroup=False, |
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verbose=None, |
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no_speech_threshold=1.0, |
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suppress_silence=False, |
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word_timestamps=True # though unused, timestamps reduce hallucination |
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).merge_all_segments() |
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adjustment = clean_transcripts( |
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result.text |
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.replace(keyword, " ") |
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.replace(header, " ") |
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) |
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mer=compute_mer(transcription, adjustment) |
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adjusted[split][sample['id']] = adjustment |
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trange.set_postfix(mer=f"{mer:.2f}", dc=len(double_check[split])) |
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if mer > 30: |
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double_check[split][sample['id']] = mer |
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print(transcription, "||", adjustment) |
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if i % 5 == 0 or i == len(unks[split]) - 1: |
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with open(f"checkpoint_{split}.json", "w") as f: |
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json.dump(adjusted[split], f) |
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``` |
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6. Replace UNK utterances with resolved ones |
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```python |
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from datasets import DatasetDict |
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import json |
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adjusted_transcripts = {} |
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for split in data_raw: |
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with open(f"checkpoint_{split}.json", "r", encoding="utf8") as f: |
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adjusted_transcripts[split] = json.load(f) |
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UNK = "[UNK]" |
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def fix_unk(sample, adjusted_dict): |
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def bad(orig, new): |
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return sacretok(new) in sacretok(orig) |
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transcription = clean_transcripts(sample['transcription'].replace(UNK, "")) |
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sid = sample['id'] |
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adjustment = adjusted_dict.get(sid, transcription) |
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if bad(transcription, adjustment): |
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# adjustment worse than just removing UNK |
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# print("skipped:", transcription, "||", adjustment) |
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adjustment = transcription |
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return {"transcription": adjustment} |
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data = DatasetDict({ |
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split: data_raw[split].map(lambda x: fix_unk(x, adjusted_transcripts[split]), load_from_cache_file=False) |
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for split in data_raw |
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}) |
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data = data.sort(["session_id","id"], load_from_cache_file=False) |
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for split in data: |
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for line in data[split]['transcription']: |
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assert UNK not in line |
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``` |
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> train adjusted 402 samples, 75 of which just removes UNKs. |
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> test adjusted 36 samples, 9 of which just removes UNKs. |
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> validation adjusted 63 samples, 7 of which just removes UNKs. |