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import os
os.system("pip install git+https://github.com/openai/whisper.git")
import gradio as gr
import whisper
from flask import Flask, jsonify, request
import requests
import numpy as np
import streamlit as st
app = Flask(__name__)
@app.route("/")
def indexApi():
return jsonify({"output": "okay"})
@app.route("/run", methods=['POST'])
def runApi():
audio_url = request.form.get("audio_url")
# key = request.form.get("key")
# model = request.form.get("model")
print(audio_url)
if (modelSelection == None):
modelSelection = "small"
model = whisper.load_model(modelSelection)
print(model)
# # reject if key not the same
# apiKey = st.secrets["Api-Key"]
# if apiKey != key:
# return jsonify({
# "image_url": image_url,
# "model": model,
# "result": "Invalid API Key",
# }), 400
response = requests.get(audio_url)
if response.status_code == requests.codes.ok:
# Only attempt to save the file if the request was successful
with open("audio.mp3", "wb") as f:
f.write(response.content)
else:
return jsonify({
"result": "Unable to save file, status code: {response.status_code}" ,
}), 400
# arr = np.asarray(bytearray(response.content), dtype=np.uint8)
result = model.transcribe("audio.mp3")
return jsonify({
"audio_url": audio_url,
"model": model,
"result": result["text"],
})
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860)
# def inference(audio):
# audio = whisper.load_audio(audio)
# audio = whisper.pad_or_trim(audio)
# mel = whisper.log_mel_spectrogram(audio).to(model.device)
# _, probs = model.detect_language(mel)
# options = whisper.DecodingOptions(fp16 = False)
# result = whisper.decode(model, mel, options)
# # print(result.text)
# return result.text, 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):
# 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")
# with gr.Group(elem_id="share-btn-container"):
# community_icon = gr.HTML(community_icon_html, visible=False)
# loading_icon = gr.HTML(loading_icon_html, visible=False)
# share_button = gr.Button("Share to community", elem_id="share-btn", visible=False)
# btn.click(inference, inputs=[audio], outputs=[text, community_icon, loading_icon, share_button])
# share_button.click(None, [], [], _js=share_js)
# 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() |