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| from transformers import pipeline | |
| import gradio as gr | |
| import time | |
| import whisper | |
| #p = pipeline("automatic-speech-recognition",model="jonatasgrosman/wav2vec2-large-xlsr-53-spanish") | |
| from transformers import WhisperProcessor, WhisperForConditionalGeneration | |
| from datasets import load_dataset | |
| #processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2") | |
| #model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2") | |
| #model.config.forced_decoder_ids = None | |
| model = whisper.load_model("large") | |
| # load model and processor | |
| #processor = WhisperProcessor.from_pretrained("openai/whisper-large-v2") | |
| #model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v2") | |
| #model.config.forced_decoder_ids = None | |
| # load dummy dataset and read audio files | |
| #ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") | |
| #sample = ds[0]["audio"] | |
| #input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features | |
| # generate token ids | |
| #predicted_ids = model.generate(input_features) | |
| # decode token ids to text | |
| #transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False) | |
| #['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>'] | |
| #transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) | |
| #[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.'] | |
| #def speech_to_text(tmp_filename, model_size): | |
| # model = whisper.load_model(model_size) | |
| # result = model.transcribe(tmp_filename) | |
| # | |
| # return result["text"] | |
| #gr.Interface( | |
| # fn=speech_to_text, | |
| # inputs=[ | |
| # gr.Audio(source="microphone", type="filepath"), | |
| # gr.Dropdown(choices=["tiny", "base", "small", "medium", "large"]), | |
| # ], | |
| # outputs="text").launch() | |
| def transcribe(language,audio, state=""):#language="Spanish", | |
| time.sleep(1) | |
| if language=="Multi": | |
| state="" | |
| result = model.transcribe(audio) | |
| text = result["text"]#processor.batch_decode(predicted_ids, skip_special_tokens=False) | |
| # if language=="Catalan": | |
| # state="" | |
| # text = pc(audio)["text"] | |
| # if language=="English": | |
| # state="" | |
| # text = pe(audio)["text"] | |
| # if language=="French": | |
| # state="" | |
| # text = pf(audio)["text"] | |
| # if language=="Japanese": | |
| # state="" | |
| # text = pj(audio)["text"] | |
| state += text + " " | |
| #text2="Esto es loq ue te he entendido" | |
| return state, state | |
| demo=gr.Interface( | |
| fn=transcribe, | |
| title="TEDCAS Offline Speech recognition", | |
| description="1)Select language 2)Click on 'record from microphone' and talk 3)Click on 'stop recording' 4)Click on submit 5)Before starting again, click on 'clear'", | |
| inputs=[ | |
| #gr.Dropdown(["Spanish","Catalan","English", "French", "Japanese"],value="Spanish"), | |
| gr.Dropdown(["Multi","Spanish"],value="Multi"), | |
| #gr.Audio(source="microphone", type="filepath", streaming=True), | |
| gr.inputs.Audio(source="microphone", type="filepath"), | |
| "state"#,"language" | |
| ], | |
| outputs=[ | |
| "textbox", | |
| "state" | |
| ], | |
| #live=True).launch() | |
| ) | |
| demo.launch() | |
| #demo.launch(auth=("TedCas", "Kike1234")) | |