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| from transformers import WhisperTokenizer | |
| import os | |
| tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small") #, language="marathi", task="transcribe" | |
| from transformers import pipeline | |
| import gradio as gr | |
| import torch | |
| pipe = pipeline(model="thak123/gom-stt-v3", #"thak123/whisper-small-LDC-V1", #"thak123/whisper-small-gom", | |
| task="automatic-speech-recognition", tokenizer= tokenizer) # change to "your-username/the-name-you-picked" | |
| # pipe.model.config.forced_decoder_ids = ( | |
| # pipe.tokenizer.get_decoder_prompt_ids( | |
| # language="marathi", task="transcribe" | |
| # ) | |
| # ) | |
| def transcribe_speech(filepath): | |
| output = pipe( | |
| filepath, | |
| max_new_tokens=256, | |
| generate_kwargs={ | |
| "task": "transcribe", | |
| "language": "konkani", | |
| }, # update with the language you've fine-tuned on | |
| chunk_length_s=30, | |
| batch_size=8, | |
| # padding=True | |
| ) | |
| return output["text"] | |
| demo = gr.Blocks() | |
| mic_transcribe = gr.Interface( | |
| fn=transcribe_speech, | |
| inputs=gr.Audio(sources="microphone", type="filepath"), | |
| outputs=gr.components.Textbox(), | |
| ) | |
| file_transcribe = gr.Interface( | |
| fn=transcribe_speech, | |
| inputs=gr.Audio(sources="upload", type="filepath"), | |
| outputs=gr.components.Textbox(), | |
| ) | |
| with demo: | |
| gr.TabbedInterface( | |
| [mic_transcribe, file_transcribe], | |
| ["Transcribe Microphone", "Transcribe Audio File"], | |
| ) | |
| demo.launch(debug=True) | |
| # # def transcribe(audio): | |
| # # # text = pipe(audio)["text"] | |
| # # # pipe(audio) | |
| # # text = pipe(audio) | |
| # # print("op",text) | |
| # # return text#pipe(audio) #text | |
| # # iface = gr.Interface( | |
| # # fn=transcribe, | |
| # # inputs=[gr.Audio(sources=["microphone", "upload"])], | |
| # # outputs="text", | |
| # # examples=[ | |
| # # [os.path.join(os.path.dirname("."),"audio/chalyaami.mp3")], | |
| # # [os.path.join(os.path.dirname("."),"audio/ekdonteen.flac")], | |
| # # [os.path.join(os.path.dirname("."),"audio/heyatachadjaale.mp3")], | |
| # # ], | |
| # # title="Whisper Konkani", | |
| # # description="Realtime demo for Konkani speech recognition using a fine-tuned Whisper small model.", | |
| # # ) | |
| # # iface.launch() | |
| # from transformers import WhisperTokenizer, pipeline | |
| # import gradio as gr | |
| # import os | |
| # tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="marathi", task="transcribe") | |
| # pipe = pipeline(model="thak123/gom-stt-v3", task="automatic-speech-recognition", tokenizer=tokenizer) | |
| # def transcribe(audio): | |
| # result = pipe(audio) | |
| # text = result[0]['text'] | |
| # print("op", text) | |
| # return text | |
| # iface = gr.Interface( | |
| # fn=transcribe, | |
| # inputs=[gr.Audio(sources=["microphone", "upload"])], | |
| # outputs="text", | |
| # examples=[ | |
| # [os.path.join(os.path.dirname("."), "audio/chalyaami.mp3")], | |
| # [os.path.join(os.path.dirname("."), "audio/ekdonteen.flac")], | |
| # [os.path.join(os.path.dirname("."), "audio/heyatachadjaale.mp3")], | |
| # ], | |
| # title="Whisper Konkani", | |
| # description="Realtime demo for Konkani speech recognition using a fine-tuned Whisper small model.", | |
| # ) | |
| # iface.launch() |