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| import gradio as gr | |
| import nltk | |
| import librosa | |
| from transformers import pipeline | |
| from transformers.file_utils import cached_path, hf_bucket_url | |
| import os, zipfile | |
| from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, Wav2Vec2Tokenizer | |
| from datasets import load_dataset | |
| import torch | |
| import kenlm | |
| import torchaudio | |
| from pyctcdecode import Alphabet, BeamSearchDecoderCTC, LanguageModel | |
| """Vietnamese speech2text""" | |
| cache_dir = './cache/' | |
| processor = Wav2Vec2Processor.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h", cache_dir=cache_dir) | |
| vi_model = Wav2Vec2ForCTC.from_pretrained("nguyenvulebinh/wav2vec2-base-vietnamese-250h", cache_dir=cache_dir) | |
| lm_file = hf_bucket_url("nguyenvulebinh/wav2vec2-base-vietnamese-250h", filename='vi_lm_4grams.bin.zip') | |
| lm_file = cached_path(lm_file,cache_dir=cache_dir) | |
| with zipfile.ZipFile(lm_file, 'r') as zip_ref: | |
| zip_ref.extractall(cache_dir) | |
| lm_file = cache_dir + 'vi_lm_4grams.bin' | |
| def get_decoder_ngram_model(tokenizer, ngram_lm_path): | |
| vocab_dict = tokenizer.get_vocab() | |
| sort_vocab = sorted((value, key) for (key, value) in vocab_dict.items()) | |
| vocab = [x[1] for x in sort_vocab][:-2] | |
| vocab_list = vocab | |
| # convert ctc blank character representation | |
| vocab_list[tokenizer.pad_token_id] = "" | |
| # replace special characters | |
| vocab_list[tokenizer.unk_token_id] = "" | |
| # vocab_list[tokenizer.bos_token_id] = "" | |
| # vocab_list[tokenizer.eos_token_id] = "" | |
| # convert space character representation | |
| vocab_list[tokenizer.word_delimiter_token_id] = " " | |
| # specify ctc blank char index, since conventially it is the last entry of the logit matrix | |
| alphabet = Alphabet.build_alphabet(vocab_list, ctc_token_idx=tokenizer.pad_token_id) | |
| lm_model = kenlm.Model(ngram_lm_path) | |
| decoder = BeamSearchDecoderCTC(alphabet, | |
| language_model=LanguageModel(lm_model)) | |
| return decoder | |
| ngram_lm_model = get_decoder_ngram_model(processor.tokenizer, lm_file) | |
| # define function to read in sound file | |
| def speech_file_to_array_fn(path, max_seconds=10): | |
| batch = {"file": path} | |
| speech_array, sampling_rate = torchaudio.load(batch["file"]) | |
| if sampling_rate != 16000: | |
| transform = torchaudio.transforms.Resample(orig_freq=sampling_rate, | |
| new_freq=16000) | |
| speech_array = transform(speech_array) | |
| speech_array = speech_array[0] | |
| if max_seconds > 0: | |
| speech_array = speech_array[:max_seconds*16000] | |
| batch["speech"] = speech_array.numpy() | |
| batch["sampling_rate"] = 16000 | |
| return batch | |
| # tokenize | |
| def speech2text_vi(audio): | |
| # read in sound file | |
| # load dummy dataset and read soundfiles | |
| ds = speech_file_to_array_fn(audio.name) | |
| # infer model | |
| input_values = processor( | |
| ds["speech"], | |
| sampling_rate=ds["sampling_rate"], | |
| return_tensors="pt" | |
| ).input_values | |
| # decode ctc output | |
| logits = vi_model(input_values).logits[0] | |
| pred_ids = torch.argmax(logits, dim=-1) | |
| greedy_search_output = processor.decode(pred_ids) | |
| beam_search_output = ngram_lm_model.decode(logits.cpu().detach().numpy(), beam_width=500) | |
| return beam_search_output | |
| """English speech2text""" | |
| nltk.download("punkt") | |
| # Loading the model and the tokenizer | |
| model_name = "facebook/wav2vec2-base-960h" | |
| eng_tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name) | |
| eng_model = Wav2Vec2ForCTC.from_pretrained(model_name) | |
| def load_data(input_file): | |
| """ Function for resampling to ensure that the speech input is sampled at 16KHz. | |
| """ | |
| # read the file | |
| speech, sample_rate = librosa.load(input_file) | |
| # make it 1-D | |
| if len(speech.shape) > 1: | |
| speech = speech[:, 0] + speech[:, 1] | |
| # Resampling at 16KHz since wav2vec2-base-960h is pretrained and fine-tuned on speech audio sampled at 16 KHz. | |
| if sample_rate != 16000: | |
| speech = librosa.resample(speech, sample_rate, 16000) | |
| return speech | |
| def correct_casing(input_sentence): | |
| """ This function is for correcting the casing of the generated transcribed text | |
| """ | |
| sentences = nltk.sent_tokenize(input_sentence) | |
| return (' '.join([s.replace(s[0], s[0].capitalize(), 1) for s in sentences])) | |
| def speech2text_en(input_file): | |
| """This function generates transcripts for the provided audio input | |
| """ | |
| speech = load_data(input_file) | |
| # Tokenize | |
| input_values = eng_tokenizer(speech, return_tensors="pt").input_values | |
| # Take logits | |
| logits = eng_model(input_values).logits | |
| # Take argmax | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| # Get the words from predicted word ids | |
| transcription = eng_tokenizer.decode(predicted_ids[0]) | |
| # Output is all upper case | |
| transcription = correct_casing(transcription.lower()) | |
| return transcription | |
| """Machine translation""" | |
| vien_model_checkpoint = "datnth1709/finetuned_HelsinkiNLP-opus-mt-vi-en_PhoMT" | |
| envi_model_checkpoint = "datnth1709/finetuned_HelsinkiNLP-opus-mt-en-vi_PhoMT" | |
| vien_translator = pipeline("translation", model=vien_model_checkpoint) | |
| envi_translator = pipeline("translation", model=envi_model_checkpoint) | |
| def translate_vi2en(Vietnamese): | |
| return vien_translator(Vietnamese)[0]['translation_text'] | |
| def translate_en2vi(English): | |
| return envi_translator(English)[0]['translation_text'] | |
| """ Inference""" | |
| def inference_vien(audio): | |
| vi_text = speech2text_vi(audio) | |
| en_text = translate_vi2en(vi_text) | |
| return vi_text, en_text | |
| def inference_envi(audio): | |
| en_text = speech2text_en(audio) | |
| vi_text = translate_en2vi(en_text) | |
| return en_text, vi_text | |
| def transcribe_vi(audio, state_vi="", state_en=""): | |
| ds = speech_file_to_array_fn(audio.name) | |
| # infer model | |
| input_values = processor( | |
| ds["speech"], | |
| sampling_rate=ds["sampling_rate"], | |
| return_tensors="pt" | |
| ).input_values | |
| # decode ctc output | |
| logits = vi_model(input_values).logits[0] | |
| pred_ids = torch.argmax(logits, dim=-1) | |
| greedy_search_output = processor.decode(pred_ids) | |
| beam_search_output = ngram_lm_model.decode(logits.cpu().detach().numpy(), beam_width=500) | |
| state_vi += beam_search_output + " " | |
| en_text = translate_vi2en(beam_search_output) | |
| state_en += en_text + " " | |
| return state_vi, state_en | |
| def transcribe_en(audio, state_en="", state_vi=""): | |
| speech = load_data(audio) | |
| # Tokenize | |
| input_values = eng_tokenizer(speech, return_tensors="pt").input_values | |
| # Take logits | |
| logits = eng_model(input_values).logits | |
| # Take argmax | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| # Get the words from predicted word ids | |
| transcription = eng_tokenizer.decode(predicted_ids[0]) | |
| # Output is all upper case | |
| transcription = correct_casing(transcription.lower()) | |
| state_en += transcription + " " | |
| vi_text = translate_en2vi(transcription) | |
| state_vi += vi_text + " " | |
| return state_en, state_vi | |
| """Gradio demo""" | |
| vi_example_text = ["Có phải bạn đang muốn tìm mua nhà ở ngoại ô thành phố Hồ Chí Minh không?", | |
| "Ánh mắt ta chạm nhau. Chỉ muốn ngắm anh lâu thật lâu.", | |
| "Nếu như một câu nói có thể khiến em vui."] | |
| vi_example_voice =[['vi_speech_01.wav'], ['vi_speech_02.wav'], ['vi_speech_03.wav']] | |
| en_example_text = ["According to a study by Statista, the global AI market is set to grow up to 54 percent every single year.", | |
| "As one of the world's greatest cities, Air New Zealand is proud to add the Big Apple to its list of 29 international destinations.", | |
| "And yet, earlier this month, I found myself at Halloween Horror Nights at Universal Orlando Resort, one of the most popular Halloween events in the US among hardcore horror buffs." | |
| ] | |
| en_example_voice =[['en_speech_01.wav'], ['en_speech_02.wav'], ['en_speech_03.wav']] | |
| with gr.Blocks() as demo: | |
| with gr.Tabs(): | |
| with gr.TabItem("Translation: Vietnamese to English"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| vietnamese_text = gr.Textbox(label="Vietnamese Text") | |
| translate_button_vien_1 = gr.Button(value="Translate To English") | |
| with gr.Column(): | |
| english_out_1 = gr.Textbox(label="English Text") | |
| translate_button_vien_1.click(lambda text: translate_vi2en(text), inputs=vietnamese_text, outputs=english_out_1) | |
| gr.Examples(examples=vi_example_text, | |
| inputs=[vietnamese_text]) | |
| with gr.TabItem("Speech2text and Vi-En Translation"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| vi_audio_1 = gr.Audio(source="microphone", label="Input Vietnamese Audio", type="file", streaming=False) | |
| translate_button_vien_2 = gr.Button(value="Translate To English") | |
| with gr.Column(): | |
| speech2text_vi1 = gr.Textbox(label="Vietnamese Text") | |
| english_out_2 = gr.Textbox(label="English Text") | |
| translate_button_vien_2.click(lambda vi_voice: inference_vien(vi_voice), inputs=vi_audio_1, outputs=[speech2text_vi1, english_out_2]) | |
| gr.Examples(examples=vi_example_voice, | |
| inputs=[vi_audio_1]) | |
| with gr.TabItem("Vi-En Realtime Translation"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| vi_audio_2 = gr.Audio(source="microphone", label="Input Vietnamese Audio", type="file", streaming=True) | |
| with gr.Column(): | |
| speech2text_vi2 = gr.Textbox(label="Vietnamese Text") | |
| english_out_3 = gr.Textbox(label="English Text") | |
| vi_audio_2.change(transcribe_vi, [vi_audio_2, speech2text_vi2, english_out_3], [speech2text_vi2, english_out_3]) | |
| with gr.Tabs(): | |
| with gr.TabItem("Translation: English to Vietnamese"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| english_text = gr.Textbox(label="English Text") | |
| translate_button_envi_1 = gr.Button(value="Translate To Vietnamese") | |
| with gr.Column(): | |
| vietnamese_out_1 = gr.Textbox(label="Vietnamese Text") | |
| translate_button_envi_1.click(lambda text: translate_en2vi(text), inputs=english_text, outputs=vietnamese_out_1) | |
| gr.Examples(examples=en_example_text, | |
| inputs=[english_text]) | |
| with gr.TabItem("Speech2text and En-Vi Translation"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| en_audio_1 = gr.Audio(source="microphone", label="Input English Audio", type="filepath", streaming=False) | |
| translate_button_envi_2 = gr.Button(value="Translate To Vietnamese") | |
| with gr.Column(): | |
| speech2text_en1 = gr.Textbox(label="English Text") | |
| vietnamese_out_2 = gr.Textbox(label="Vietnamese Text") | |
| translate_button_envi_2.click(lambda en_voice: inference_envi(en_voice), inputs=en_audio_1, outputs=[speech2text_en1, vietnamese_out_2]) | |
| gr.Examples(examples=en_example_voice, | |
| inputs=[en_audio_1]) | |
| with gr.TabItem("En-Vi Realtime Translation"): | |
| with gr.Row(): | |
| with gr.Column(): | |
| en_audio_2 = gr.Audio(source="microphone", label="Input English Audio", type="filepath", streaming=True) | |
| with gr.Column(): | |
| speech2text_en2 = gr.Textbox(label="English Text") | |
| vietnamese_out_3 = gr.Textbox(label="Vietnamese Text") | |
| en_audio_2.change(transcribe_en, [en_audio_2, speech2text_en2, vietnamese_out_3], [speech2text_en2, vietnamese_out_3]) | |
| if __name__ == "__main__": | |
| demo.launch() |