app.py

import os
from pprint import pprint
os.system("pip install git+https://github.com/openai/whisper.git")
import gradio as gr
import whisper
from transformers import pipeline
import torch
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
# import streaming.py
# from next_word_prediction import GPT2



### code snippet
gpt2 = AutoModelForCausalLM.from_pretrained("gpt2", return_dict_in_generate=True)
tokenizer = AutoTokenizer.from_pretrained("gpt2")

### /code snippet

from share_btn import community_icon_html, loading_icon_html, share_js

# get gpt2 model
generator = pipeline('text-generation', model='gpt2')

# whisper model specification 
model = whisper.load_model("tiny")

def buttonValues(value):
    value = "Hello"
    return value
        
def inference(audio):
    # load audio data
    audio = whisper.load_audio(audio)
    # ensure sample is in correct format for inference
    audio = whisper.pad_or_trim(audio)

    # generate a log-mel spetrogram of the audio data
    mel = whisper.log_mel_spectrogram(audio).to(model.device)
    
    _, probs = model.detect_language(mel)

    # decode audio data
    options = whisper.DecodingOptions(fp16 = False)
    # transcribe speech to text
    result = whisper.decode(model, mel, options)
    
    # Added prompt below
    input_prompt = "The following is a transcript of someone talking, please predict what they will say next. \n"
    ### code 
    input_total = input_prompt + result.text
    input_ids = tokenizer(input_total, return_tensors="pt").input_ids
    print("inputs ", input_ids)

    # prompt length
    # prompt_length = len(tokenizer.decode(inputs_ids[0]))
    
    # length penalty for gpt2.generate??? 
    #Prompt
    generated_outputs = gpt2.generate(input_ids, do_sample=True, num_return_sequences=3, output_scores=True)
    print("outputs generated ", generated_outputs[0])
    # only use id's that were generated
    # gen_sequences has shape [3, 15]
    gen_sequences = generated_outputs.sequences[:, input_ids.shape[-1]:]
    print("gen sequences: ", gen_sequences)
    
    # let's stack the logits generated at each step to a tensor and transform
    # logits to probs
    probs = torch.stack(generated_outputs.scores, dim=1).softmax(-1)  # -> shape [3, 15, vocab_size]
    
    # now we need to collect the probability of the generated token
    # we need to add a dummy dim in the end to make gather work
    gen_probs = torch.gather(probs, 2, gen_sequences[:, :, None]).squeeze(-1)
    print("gen probs result: ", gen_probs)
    # now we can do all kinds of things with the probs
    
    # 1) the probs that exactly those sequences are generated again
    # those are normally going to be very small
    # unique_prob_per_sequence = gen_probs.prod(-1)
    
    # 2) normalize the probs over the three sequences
    # normed_gen_probs = gen_probs / gen_probs.sum(0)
    # assert normed_gen_probs[:, 0].sum() == 1.0, "probs should be normalized"
    
    # 3) compare normalized probs to each other like in 1)
    # unique_normed_prob_per_sequence = normed_gen_probs.prod(-1)
    
    ### end code
    # print audio data as text
    # print(result.text)
    # prompt
    getText = generator(result.text, max_new_tokens=10, num_return_sequences=5)
    # pprint("getText: ", getText)
    # pprint("text.result: ", result.text)
    # result.text
    return getText, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)



css = """
        .gradio-container {
            font-family: 'IBM Plex Sans', sans-serif;
        }
        .gr-button {
            color: white;
            border-color: black;
            background: black;
        }
        input[type='range'] {
            accent-color: black;
        }
        .dark input[type='range'] {
            accent-color: #dfdfdf;
        }
        .container {
            max-width: 730px;
            margin: auto;
            padding-top: 1.5rem;
        }
     
        .details:hover {
            text-decoration: underline;
        }
        .gr-button {
            white-space: nowrap;
        }
        .gr-button:focus {
            border-color: rgb(147 197 253 / var(--tw-border-opacity));
            outline: none;
            box-shadow: var(--tw-ring-offset-shadow), var(--tw-ring-shadow), var(--tw-shadow, 0 0 #0000);
            --tw-border-opacity: 1;
            --tw-ring-offset-shadow: var(--tw-ring-inset) 0 0 0 var(--tw-ring-offset-width) var(--tw-ring-offset-color);
            --tw-ring-shadow: var(--tw-ring-inset) 0 0 0 calc(3px var(--tw-ring-offset-width)) var(--tw-ring-color);
            --tw-ring-color: rgb(191 219 254 / var(--tw-ring-opacity));
            --tw-ring-opacity: .5;
        }
        .footer {
            margin-bottom: 45px;
            margin-top: 35px;
            text-align: center;
            border-bottom: 1px solid #e5e5e5;
        }
        .footer>p {
            font-size: .8rem;
            display: inline-block;
            padding: 0 10px;
            transform: translateY(10px);
            background: white;
        }
        .dark .footer {
            border-color: #303030;
        }
        .dark .footer>p {
            background: #0b0f19;
        }
        .prompt h4{
            margin: 1.25em 0 .25em 0;
            font-weight: bold;
            font-size: 115%;
        }
        .animate-spin {
            animation: spin 1s linear infinite;
        }
        @keyframes spin {
            from {
                transform: rotate(0deg);
            }
            to {
                transform: rotate(360deg);
            }
        }
        #share-btn-container {
            display: flex; margin-top: 1.5rem !important; padding-left: 0.5rem !important; padding-right: 0.5rem !important; background-color: #000000; justify-content: center; align-items: center; border-radius: 9999px !important; width: 13rem;
        }
        #share-btn {
            all: initial; color: #ffffff;font-weight: 600; cursor:pointer; font-family: 'IBM Plex Sans', sans-serif; margin-left: 0.5rem !important; padding-top: 0.25rem !important; padding-bottom: 0.25rem !important;
        }
        #share-btn * {
            all: unset;
        }
"""

block = gr.Blocks(css=css)



with block:
    gr.HTML(
        """
            <div style="text-align: center; max-width: 650px; margin: 0 auto;">
              <div
                style="
                  display: inline-flex;
                  align-items: center;
                  gap: 0.8rem;
                  font-size: 1.75rem;
                "
              >
                <svg
                  width="0.65em"
                  height="0.65em"
                  viewBox="0 0 115 115"
                  fill="none"
                  xmlns="http://www.w3.org/2000/svg"
                >
                  <rect width="23" height="23" fill="white"></rect>
                  <rect y="69" width="23" height="23" fill="white"></rect>
                  <rect x="23" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="23" y="69" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="46" width="23" height="23" fill="white"></rect>
                  <rect x="46" y="69" width="23" height="23" fill="white"></rect>
                  <rect x="69" width="23" height="23" fill="black"></rect>
                  <rect x="69" y="69" width="23" height="23" fill="black"></rect>
                  <rect x="92" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="92" y="69" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="115" y="46" width="23" height="23" fill="white"></rect>
                  <rect x="115" y="115" width="23" height="23" fill="white"></rect>
                  <rect x="115" y="69" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="92" y="46" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="92" y="115" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="92" y="69" width="23" height="23" fill="white"></rect>
                  <rect x="69" y="46" width="23" height="23" fill="white"></rect>
                  <rect x="69" y="115" width="23" height="23" fill="white"></rect>
                  <rect x="69" y="69" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="46" y="46" width="23" height="23" fill="black"></rect>
                  <rect x="46" y="115" width="23" height="23" fill="black"></rect>
                  <rect x="46" y="69" width="23" height="23" fill="black"></rect>
                  <rect x="23" y="46" width="23" height="23" fill="#D9D9D9"></rect>
                  <rect x="23" y="115" width="23" height="23" fill="#AEAEAE"></rect>
                  <rect x="23" y="69" width="23" height="23" fill="black"></rect>
                </svg>
                <h1 style="font-weight: 900; margin-bottom: 7px;">
                  Whisper
                </h1>
              </div>
              <p style="margin-bottom: 10px; font-size: 94%">
                Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. This demo cuts audio after around 30 secs.
              </p>
               <p>You can skip the queue by using google colab for the space: <a href="https://colab.research.google.com/drive/1WJ98KHgZxFGrHiMm4TyWZllSew_Af_ff?usp=sharing"><img data-canonical-src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667"></a></p>
            </div>
        """
    )
    with gr.Group():
        with gr.Box():
            with gr.Row().style(mobile_collapse=False, equal_height=True):
                # get audio from microphone 
                audio = gr.Audio(
                    label="Input Audio",
                    show_label=False,
                    source="microphone",
                    type="filepath"
                )

                btn = gr.Button("Transcribe")
        text = gr.Textbox(show_label=False, elem_id="result-textarea")
        # added rText below
        # rText = gr.Textbox(show_label=False, elem_id="result-textarea")

        

        buttonV = gr.Button("  ")
        buttonV.click(buttonValues, inputs=[], outputs=[])
        
        btn.click(inference, inputs=[audio], outputs=[text])
 
        gr.HTML('''
        <div class="footer">
                    <p>Model by <a href="https://github.com/openai/whisper" style="text-decoration: underline;" target="_blank">OpenAI</a> - Gradio Demo by 🤗 Hugging Face
                    </p>
        </div>
        ''')

block.launch()