t5-small wav2vec2 grammar fixer model and tokenizer

README.md

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+# flexudy-pipe-question-generation-v2
+After transcribing your audio with Wav2Vec2, you might be interested in a post processor.
+
+I trained it with only 42K paragraphs from the SQUAD dataset. All paragraphs had at most 128 tokens (separated by white spaces)
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+model_name = "flexudy/t5-small-wav2vec2-grammar-fixer"
+
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+sent = """GOING ALONG SLUSHY COUNTRY ROADS AND SPEAKING TO DAMP AUDIENCES IN DRAUGHTY SCHOOL ROOMS DAY AFTER DAY FOR A FORTNIGHT HE'LL HAVE TO PUT IN AN APPEARANCE AT SOME PLACE OF WORSHIP ON SUNDAY MORNING AND HE CAN COME TO US IMMEDIATELY AFTERWARDS"""
+
+input_text = "fix: { " + sent + " } </s>"
+
+input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=256, truncation=True, add_special_tokens=True)
+
+outputs = model.generate(
+    input_ids=input_ids,
+    max_length=256,
+    num_beams=4,
+    repetition_penalty=1.0,
+    length_penalty=1.0,
+    early_stopping=True
+)
+
+sentence = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+
+print(f"{sentence}")
+```
+
+INPUT 1:
+```
+BEFORE HE HAD TIME TO ANSWER A MUCH ENCUMBERED VERA BURST INTO THE ROOM WITH THE QUESTION I SAY CAN I LEAVE THESE HERE IN TWO THOUSAND AND TWO THESE WERE A SMALL BLACK PIG AND A LUSTY SPECIMEN OF BLACK RED GAME COCK
+```
+OUTPUT 1:
+```
+Before he had time to answer a much-enumbered era burst into the room with the question, I say, "Can I leave these here?" In 2002, these were a small black pig and a dusty specimen of black red game cock.
+```
+
+INPUT 2:
+```
+GOING ALONG SLUSHY COUNTRY ROADS AND SPEAKING TO DAMP AUDIENCES IN DRAUGHTY SCHOOL ROOMS DAY AFTER DAY FOR A FORTNIGHT HE'LL HAVE TO PUT IN AN APPEARANCE AT SOME PLACE OF WORSHIP ON SUNDAY MORNING AND HE CAN COME TO US IMMEDIATELY AFTERWARDS
+```
+
+OUTPUT 2:
+```
+Going along Slushy Country Roads and speaking to damp audiences in Droughty School rooms day after day for a fortnight, he'll have to put in an appearance at some place of worship on Sunday morning and he can come to us immediately afterwards.
+```
+I strongly recommend improving the performance via further fine-tuning or by training more examples.
+- Possible Quick Rule based improvements: Align the transcribed version and the generated version. If the similarity of two words (case-insensitive) vary by more than some threshold based on some similarity metric (e.g. Levenshtein), then keep the transcribed word.