Spaces:
Runtime error
Runtime error
| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from datasets import load_dataset, Audio | |
| from transformers import EncodecModel, AutoProcessor | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # load a demonstration datasets | |
| librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") | |
| # load the model + processor (for pre-processing the audio) | |
| model = EncodecModel.from_pretrained("facebook/encodec_24khz") | |
| processor = AutoProcessor.from_pretrained("facebook/encodec_24khz") | |
| # cast the audio data to the correct sampling rate for the model | |
| librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate)) | |
| audio_sample = librispeech_dummy[0]["audio"]["array"] | |
| # pre-process the inputs | |
| inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt") | |
| # explicitly encode then decode the audio inputs | |
| encoder_outputs = model.encode(inputs["input_values"], inputs["padding_mask"]) | |
| audio_values = model.decode(encoder_outputs.audio_codes, encoder_outputs.audio_scales, inputs["padding_mask"])[0] | |
| # or the equivalent with a forward pass | |
| audio_values = model(inputs["input_values"], inputs["padding_mask"]).audio_values | |
| # def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| # ): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # messages.append({"role": "user", "content": message}) | |
| # response = "" | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = message.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| # """ | |
| # For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| # """ | |
| # demo = gr.ChatInterface( | |
| # respond, | |
| # additional_inputs=[ | |
| # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| # gr.Slider( | |
| # minimum=0.1, | |
| # maximum=1.0, | |
| # value=0.95, | |
| # step=0.05, | |
| # label="Top-p (nucleus sampling)", | |
| # ), | |
| # ], | |
| # ) | |
| if __name__ == "__main__": | |
| demo.launch() |