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| from transformers import pipeline | |
| import gradio as gr | |
| import pyewts | |
| converter = pyewts.pyewts() | |
| # def remove_repeated_words(text): | |
| # # Tokenize the input text into words | |
| # words = text.split() | |
| # # Create a dictionary to count word occurrences | |
| # word_count = {} | |
| # # Create a list to store the final words | |
| # new_words = [] | |
| # for word in words: | |
| # # Check if the word is in the dictionary | |
| # if word in word_count: | |
| # # If it has occurred once before, add it to the list with a count of 2 | |
| # if word_count[word] == 1: | |
| # new_words.append(word) | |
| # word_count[word] = 2 | |
| # else: | |
| # # If it has not occurred before, add it to the dictionary with a count of 1 | |
| # word_count[word] = 1 | |
| # new_words.append(word) | |
| # result = ' '.join(new_words) | |
| # return result | |
| # pipe = pipeline(model="openpecha/whisper-small",device='cuda') | |
| # pipe = pipeline(model="TenzinGayche/whisper-small-3",device='cuda') | |
| pipe = pipeline(model="spsither/whipser-small-r2",device='cuda') | |
| def transcribe(microphone, upload): | |
| if(microphone): | |
| audio = microphone | |
| else: | |
| audio = upload | |
| text = pipe(audio)["text"] | |
| # text = remove_repeated_words(text) | |
| state = converter.toUnicode(text) | |
| return state,audio | |
| # Set the starting state to an empty string | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=[gr.Audio(source="microphone", type="filepath"),gr.Audio(source="upload", type="filepath")], | |
| outputs=["text","audio"], | |
| title="Whisper Small Tibetan", | |
| description="Realtime demo for Tibetan speech recognition using a fine-tuned Whisper medium model. Feedbacks: https://forms.gle/psbZnXGeBWXptkvs9", | |
| ) | |
| iface.launch() | |