| |
| """demo 2/3.ipynb |
| |
| Automatically generated by Colaboratory. |
| |
| Original file is located at |
| https://colab.research.google.com/drive/1QeNS57tZzvJudeNjQczKJ-PbN0l1tK6V |
| |
| # Import library |
| """ |
| import os |
| import librosa |
| import gradio as gr |
| import noisereduce as nr |
| from scipy.io import wavfile |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration |
|
|
| """# Load model""" |
|
|
| from google.colab import drive |
| import os |
| drive.mount('/content/gdrive') |
|
|
| |
| processor = WhisperProcessor.from_pretrained("/content/gdrive/MyDrive/ColabNotebookShared/Speech2TextHuyenNguyen/Model/FPTVinTest2") |
| model = WhisperForConditionalGeneration.from_pretrained("/content/gdrive/MyDrive/ColabNotebookShared/Speech2TextHuyenNguyen/Model/FPTVinTest2/checkpoint-1332").to("cuda") |
| model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(task = "transcribe") |
|
|
| """# Slipt audio""" |
|
|
| from pydub import AudioSegment |
|
|
| def preprocessing(path): |
| |
| type_file = path.split(".")[-1] |
| sound = AudioSegment.from_file(path, type_file) |
| path_list = [] |
|
|
| |
| time_audio = int(sound.duration_seconds / 20) + 1 |
| for i in range(time_audio): |
| t1 = i * 20 * 1000 |
| t2 = (i+1) * 20 * 1000 |
| if i == (time_audio-1): |
| newAudio = sound[t1:] |
| else: |
| newAudio = sound[t1:t2] |
|
|
| newAudio = newAudio.split_to_mono()[0] |
| newAudio = newAudio.set_frame_rate(16000) |
| audio_path = '/content/new_audio' + str(i) + '.wav' |
| newAudio.export(audio_path, format="wav") |
| path_list.append(audio_path) |
| return path_list |
|
|
| """# Capitalization""" |
|
|
| !git lfs install |
| !git clone https://github.com/huyenxam/Vicap.git |
|
|
| |
| |
|
|
| import os |
| from gec_model import GecBERTModel |
|
|
| cache_dir = "./" |
| model_cap = GecBERTModel( |
| vocab_path=os.path.join(cache_dir, "vocabulary"), |
| model_paths="dragonSwing/vibert-capu", |
| split_chunk=True |
| ) |
|
|
| """# Spelling Correction""" |
|
|
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| tokenizer_spell = AutoTokenizer.from_pretrained("VietAI/vit5-base") |
| model_spell = AutoModelForSeq2SeqLM.from_pretrained("HuyenNguyen/Vi-test1") |
| model_spell.cuda() |
|
|
| def spelling_text(text): |
| encoding = tokenizer_spell(text, return_tensors="pt") |
| input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda") |
| outputs = model_spell.generate( |
| input_ids=input_ids, attention_mask=attention_masks, |
| max_length=30, |
| ) |
|
|
| for output in outputs: |
| line = tokenizer_spell.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True) |
| return line |
|
|
| def spelling(transcription): |
| sentences = transcription.split(" ") |
| len_sen = int(len(sentences) / 25) + 1 |
| result = "" |
| for i in range(len_sen): |
| t1 = i * 24 |
| t2 = (i+1) * 24 |
| if i == (len_sen - 1): |
| text = " ".join(sentences[t1:]) |
| else: |
| text = " ".join(sentences[t1:t2]) |
| result = result + " " + spelling_text(text) |
| |
| return result |
|
|
| """# Speech To Text""" |
|
|
| import torch |
| import numpy as np |
| import gradio as gr |
| from scipy.io.wavfile import write |
| import pytube as pt |
| from transformers import pipeline |
| from huggingface_hub import model_info |
|
|
| def transcribe(microphone, file_upload): |
| warn_output = "" |
| if (microphone is not None) and (file_upload is not None): |
| warn_output = ( |
| "WARNING: You've uploaded an audio file and used the microphone. " |
| "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n" |
| ) |
|
|
| elif (microphone is None) and (file_upload is None): |
| return "ERROR: You have to either use the microphone or upload an audio file" |
|
|
| path = microphone if microphone is not None else file_upload |
|
|
| X_new, sr_new = librosa.load(path) |
| dst = "/content/audio.wav" |
| write(dst, sr_new, X_new) |
|
|
| |
| transcription = "" |
| path_list = preprocessing(dst) |
| for audio_path in path_list: |
| |
| X, sr = librosa.load(audio_path, sr=16000) |
| input_features = processor(X.astype('float16'), return_tensors="pt").input_features |
| |
| predicted_ids = model.generate(input_features.to("cuda")) |
| text = processor.batch_decode(predicted_ids, skip_special_tokens = True)[0] |
| transcription = transcription + " " + text |
|
|
| transcription_spell = spelling(transcription) |
| |
| transcription_cap = model_cap(transcription_spell)[0] |
|
|
| |
| return transcription_cap |
|
|
|
|
| def _return_yt_html_embed(yt_url): |
| video_id = yt_url.split("?v=")[-1] |
| HTML_str = ( |
| f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' |
| " </center>" |
| ) |
| return HTML_str |
|
|
|
|
| def yt_transcribe(yt_url): |
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| return "ouput", 'This feature is temporarily locked' |
|
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|
|
| demo = gr.Blocks() |
|
|
| mf_transcribe = gr.Interface( |
| fn=transcribe, |
| inputs=[ |
| gr.inputs.Audio(source="microphone", type="filepath", optional=True), |
| gr.inputs.Audio(source="upload", type="filepath", optional=True), |
| ], |
| outputs="text", |
| layout="horizontal", |
| theme="huggingface", |
| title="PYLAB Demo: Transcribe Audio", |
| allow_flagging="never", |
| ) |
|
|
| yt_transcribe = gr.Interface( |
| fn=yt_transcribe, |
| inputs=[gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")], |
| outputs=["html", "text"], |
| layout="horizontal", |
| theme="huggingface", |
| title="PYLAB Demo: Transcribe YouTube", |
| allow_flagging="never", |
| ) |
|
|
| with demo: |
| gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"]) |
|
|
| demo.launch(enable_queue=True) |
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|
