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Update app.py
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app.py
CHANGED
@@ -2,30 +2,6 @@ import gradio as gr
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from transformers import pipeline
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import librosa
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########################ASR model###############################
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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# load model and processor
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processor = WhisperProcessor.from_pretrained("openai/whisper-base")
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model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base")
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model.config.forced_decoder_ids = None
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sample_rate = 16000
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def ASR_model(audio, sr=16000):
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DB_audio = audio
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input_features = processor(audio, sampling_rate=sr, return_tensors="pt").input_features
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# generate token ids
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predicted_ids = model.generate(input_features)
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# decode token ids to text
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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return transcription
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########################LLama model###############################
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from transformers import AutoModelForCausalLM, AutoTokenizer
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@@ -69,6 +45,31 @@ def RallyRespone(chat_history, message):
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res = t_chat[t_chat.rfind("Rally: "):]
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return res
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########################Gradio UI###############################
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# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
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from transformers import pipeline
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import librosa
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########################LLama model###############################
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from transformers import AutoModelForCausalLM, AutoTokenizer
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res = t_chat[t_chat.rfind("Rally: "):]
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return res
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########################ASR model###############################
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from transformers import WhisperProcessor, WhisperForConditionalGeneration
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# load model and processor
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processor = WhisperProcessor.from_pretrained("openai/whisper-base")
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model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-base")
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model.config.forced_decoder_ids = None
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sample_rate = 16000
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def ASR_model(audio, sr=16000):
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DB_audio = audio
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input_features = processor(audio, sampling_rate=sr, return_tensors="pt").input_features
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# generate token ids
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predicted_ids = model.generate(input_features)
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# decode token ids to text
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
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transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
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return transcription
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########################Gradio UI###############################
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# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.
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