Whisper stt overhaul js (#6194)

---------

Co-authored-by: RandoInternetPreson <aaronalai1@gmail.com>
This commit is contained in:
TimStrauven 2024-07-02 04:27:18 +02:00 committed by GitHub
parent 8a39f579d8
commit 8074fba18d
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2 changed files with 143 additions and 52 deletions

View File

@ -1,25 +1,86 @@
var recButton = document.getElementsByClassName("record-button")[0].cloneNode(true); console.log("Whisper STT script loaded");
let mediaRecorder;
let audioChunks = [];
let isRecording = false;
window.startStopRecording = function() {
if (!navigator.mediaDevices || !navigator.mediaDevices.getUserMedia) {
console.error("getUserMedia not supported on your browser!");
return;
}
if (isRecording == false) {
//console.log("Start recording function called");
navigator.mediaDevices.getUserMedia({ audio: true })
.then(stream => {
//console.log("Got audio stream");
mediaRecorder = new MediaRecorder(stream);
audioChunks = []; // Reset audio chunks
mediaRecorder.start();
//console.log("MediaRecorder started");
recButton.icon;
recordButton.innerHTML = recButton.innerHTML = "Stop";
isRecording = true;
mediaRecorder.addEventListener("dataavailable", event => {
//console.log("Data available event, data size: ", event.data.size);
audioChunks.push(event.data);
});
mediaRecorder.addEventListener("stop", () => {
//console.log("MediaRecorder stopped");
if (audioChunks.length > 0) {
const audioBlob = new Blob(audioChunks, { type: "audio/webm" });
//console.log("Audio blob created, size: ", audioBlob.size);
const reader = new FileReader();
reader.readAsDataURL(audioBlob);
reader.onloadend = function() {
const base64data = reader.result;
//console.log("Audio converted to base64, length: ", base64data.length);
const audioBase64Input = document.querySelector("#audio-base64 textarea");
if (audioBase64Input) {
audioBase64Input.value = base64data;
audioBase64Input.dispatchEvent(new Event("input", { bubbles: true }));
audioBase64Input.dispatchEvent(new Event("change", { bubbles: true }));
//console.log("Updated textarea with base64 data");
} else {
console.error("Could not find audio-base64 textarea");
}
};
} else {
console.error("No audio data recorded for Whisper");
}
});
});
} else {
//console.log("Stopping MediaRecorder");
recordButton.innerHTML = recButton.innerHTML = "Rec.";
isRecording = false;
mediaRecorder.stop();
}
};
const recordButton = gradioApp().querySelector("#record-button");
recordButton.addEventListener("click", window.startStopRecording);
function gradioApp() {
const elems = document.getElementsByTagName("gradio-app");
const gradioShadowRoot = elems.length == 0 ? null : elems[0].shadowRoot;
return gradioShadowRoot ? gradioShadowRoot : document;
}
// extra rec button next to generate button
var recButton = recordButton.cloneNode(true);
var generate_button = document.getElementById("Generate"); var generate_button = document.getElementById("Generate");
generate_button.insertAdjacentElement("afterend", recButton); generate_button.insertAdjacentElement("afterend", recButton);
recButton.style.setProperty("margin-left", "-10px"); recButton.style.setProperty("margin-left", "-10px");
recButton.innerText = "Rec."; recButton.innerHTML = "Rec.";
recButton.addEventListener("click", function() { recButton.addEventListener("click", function() {
var originalRecordButton = document.getElementsByClassName("record-button")[1]; recordButton.click();
originalRecordButton.click(); });
var stopRecordButtons = document.getElementsByClassName("stop-button");
if (stopRecordButtons.length > 1) generate_button.parentElement.removeChild(stopRecordButtons[0]);
var stopRecordButton = document.getElementsByClassName("stop-button")[0];
generate_button.insertAdjacentElement("afterend", stopRecordButton);
//stopRecordButton.style.setProperty("margin-left", "-10px");
stopRecordButton.style.setProperty("padding-right", "10px");
recButton.style.display = "none";
stopRecordButton.addEventListener("click", function() {
recButton.style.display = "flex";
});
});

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@ -1,8 +1,13 @@
import base64
import gc
import io
from pathlib import Path from pathlib import Path
import gradio as gr import gradio as gr
import speech_recognition as sr
import numpy as np import numpy as np
import torch
import whisper
from pydub import AudioSegment
from modules import shared from modules import shared
@ -11,13 +16,16 @@ input_hijack = {
'value': ["", ""] 'value': ["", ""]
} }
# parameters which can be customized in settings.json of webui # parameters which can be customized in settings.yaml of webui
params = { params = {
'whipser_language': 'english', 'whipser_language': 'english',
'whipser_model': 'small.en', 'whipser_model': 'small.en',
'auto_submit': True 'auto_submit': True
} }
startup_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
WHISPERMODEL = whisper.load_model(params['whipser_model'], device=startup_device)
def chat_input_modifier(text, visible_text, state): def chat_input_modifier(text, visible_text, state):
global input_hijack global input_hijack
@ -28,54 +36,76 @@ def chat_input_modifier(text, visible_text, state):
return text, visible_text return text, visible_text
def do_stt(audio, whipser_model, whipser_language): def do_stt(audio, whipser_language):
transcription = "" # use pydub to convert sample_rate and sample_width for whisper input
r = sr.Recognizer() dubaudio = AudioSegment.from_file(io.BytesIO(audio))
dubaudio = dubaudio.set_channels(1)
dubaudio = dubaudio.set_frame_rate(16000)
dubaudio = dubaudio.set_sample_width(2)
# Convert to AudioData # same method to get the array as openai whisper repo used from wav file
audio_data = sr.AudioData(sample_rate=audio[0], frame_data=audio[1], sample_width=4) audio_np = np.frombuffer(dubaudio.raw_data, np.int16).flatten().astype(np.float32) / 32768.0
try: if len(whipser_language) == 0:
transcription = r.recognize_whisper(audio_data, language=whipser_language, model=whipser_model) result = WHISPERMODEL.transcribe(audio=audio_np)
except sr.UnknownValueError: else:
print("Whisper could not understand audio") result = WHISPERMODEL.transcribe(audio=audio_np, language=whipser_language)
except sr.RequestError as e: return result["text"]
print("Could not request results from Whisper", e)
def auto_transcribe(audio, auto_submit, whipser_language):
if audio is None or audio == "":
print("Whisper received no audio data")
return "", ""
audio_bytes = base64.b64decode(audio.split(',')[1])
transcription = do_stt(audio_bytes, whipser_language)
if auto_submit:
input_hijack.update({"state": True, "value": [transcription, transcription]})
return transcription return transcription
def auto_transcribe(audio, auto_submit, whipser_model, whipser_language): def reload_whispermodel(whisper_model_name: str, whisper_language: str, device: str):
if audio is None: if len(whisper_model_name) > 0:
return "", "" global WHISPERMODEL
sample_rate, audio_data = audio WHISPERMODEL = None
if not isinstance(audio_data[0], np.ndarray): # workaround for chrome audio. Mono? if torch.cuda.is_available():
# Convert to 2 channels, so each sample s_i consists of the same value in both channels [val_i, val_i] torch.cuda.empty_cache()
audio_data = np.column_stack((audio_data, audio_data)) gc.collect()
audio = (sample_rate, audio_data)
transcription = do_stt(audio, whipser_model, whipser_language)
if auto_submit:
input_hijack.update({"state": True, "value": [transcription, transcription]})
return transcription, None if device != "none":
if device == "cuda":
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
WHISPERMODEL = whisper.load_model(whisper_model_name, device=device)
params.update({"whipser_model": whisper_model_name})
if ".en" in whisper_model_name:
whisper_language = "english"
audio_update = gr.Audio.update(interactive=True)
else:
audio_update = gr.Audio.update(interactive=False)
return [whisper_model_name, whisper_language, str(device), audio_update]
def ui(): def ui():
with gr.Accordion("Whisper STT", open=True): with gr.Accordion("Whisper STT", open=True):
with gr.Row(): with gr.Row():
audio = gr.Audio(source="microphone", type="numpy") audio = gr.Textbox(elem_id="audio-base64", visible=False)
record_button = gr.Button("Rec.", elem_id="record-button", elem_classes="custom-button")
with gr.Row(): with gr.Row():
with gr.Accordion("Settings", open=False): with gr.Accordion("Settings", open=False):
auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=params['auto_submit']) auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=params['auto_submit'])
whipser_model = gr.Dropdown(label='Whisper Model', value=params['whipser_model'], choices=["tiny.en", "base.en", "small.en", "medium.en", "tiny", "base", "small", "medium", "large"]) device_dropd = gr.Dropdown(label='Device', value=str(startup_device), choices=["cuda", "cpu", "none"])
whipser_language = gr.Dropdown(label='Whisper Language', value=params['whipser_language'], choices=["chinese", "german", "spanish", "russian", "korean", "french", "japanese", "portuguese", "turkish", "polish", "catalan", "dutch", "arabic", "swedish", "italian", "indonesian", "hindi", "finnish", "vietnamese", "hebrew", "ukrainian", "greek", "malay", "czech", "romanian", "danish", "hungarian", "tamil", "norwegian", "thai", "urdu", "croatian", "bulgarian", "lithuanian", "latin", "maori", "malayalam", "welsh", "slovak", "telugu", "persian", "latvian", "bengali", "serbian", "azerbaijani", "slovenian", "kannada", "estonian", "macedonian", "breton", "basque", "icelandic", "armenian", "nepali", "mongolian", "bosnian", "kazakh", "albanian", "swahili", "galician", "marathi", "punjabi", "sinhala", "khmer", "shona", "yoruba", "somali", "afrikaans", "occitan", "georgian", "belarusian", "tajik", "sindhi", "gujarati", "amharic", "yiddish", "lao", "uzbek", "faroese", "haitian creole", "pashto", "turkmen", "nynorsk", "maltese", "sanskrit", "luxembourgish", "myanmar", "tibetan", "tagalog", "malagasy", "assamese", "tatar", "hawaiian", "lingala", "hausa", "bashkir", "javanese", "sundanese"]) whisper_model_dropd = gr.Dropdown(label='Whisper Model', value=params['whipser_model'], choices=["tiny.en", "base.en", "small.en", "medium.en", "tiny", "base", "small", "medium", "large"])
whisper_language = gr.Dropdown(label='Whisper Language', value=params['whipser_language'], choices=["english", "chinese", "german", "spanish", "russian", "korean", "french", "japanese", "portuguese", "turkish", "polish", "catalan", "dutch", "arabic", "swedish", "italian", "indonesian", "hindi", "finnish", "vietnamese", "hebrew", "ukrainian", "greek", "malay", "czech", "romanian", "danish", "hungarian", "tamil", "norwegian", "thai", "urdu", "croatian", "bulgarian", "lithuanian", "latin", "maori", "malayalam", "welsh", "slovak", "telugu", "persian", "latvian", "bengali", "serbian", "azerbaijani", "slovenian", "kannada", "estonian", "macedonian", "breton", "basque", "icelandic", "armenian", "nepali", "mongolian", "bosnian", "kazakh", "albanian", "swahili", "galician", "marathi", "punjabi", "sinhala", "khmer", "shona", "yoruba", "somali", "afrikaans", "occitan", "georgian", "belarusian", "tajik", "sindhi", "gujarati", "amharic", "yiddish", "lao", "uzbek", "faroese", "haitian creole", "pashto", "turkmen", "nynorsk", "maltese", "sanskrit", "luxembourgish", "myanmar", "tibetan", "tagalog", "malagasy", "assamese", "tatar", "hawaiian", "lingala", "hausa", "bashkir", "javanese", "sundanese"])
audio.stop_recording( audio.change(
auto_transcribe, [audio, auto_submit, whipser_model, whipser_language], [shared.gradio['textbox'], audio]).then( auto_transcribe, [audio, auto_submit, whisper_language], [shared.gradio['textbox']]).then(
None, auto_submit, None, js="(check) => {if (check) { document.getElementById('Generate').click() }}") None, auto_submit, None, _js="(check) => {if (check) { document.getElementById('Generate').click() }}")
whipser_model.change(lambda x: params.update({"whipser_model": x}), whipser_model, None) device_dropd.input(reload_whispermodel, [whisper_model_dropd, whisper_language, device_dropd], [whisper_model_dropd, whisper_language, device_dropd, audio])
whipser_language.change(lambda x: params.update({"whipser_language": x}), whipser_language, None) whisper_model_dropd.change(reload_whispermodel, [whisper_model_dropd, whisper_language, device_dropd], [whisper_model_dropd, whisper_language, device_dropd, audio])
whisper_language.change(lambda x: params.update({"whipser_language": x}), whisper_language, None)
auto_submit.change(lambda x: params.update({"auto_submit": x}), auto_submit, None) auto_submit.change(lambda x: params.update({"auto_submit": x}), auto_submit, None)