import os
import gradio as gr
import requests
import langid
import base64
import json
import time
import re
import hashlib
import hash_code_for_cached_output
API_URL = os.environ.get("API_URL")
supported_languages = ['zh', 'en', 'ja', 'ko', 'es', 'fr']
supported_styles = {
'zh': "zh_default",
'en': [
"en_default",
"en_us",
"en_br",
"en_au",
"en_in"
],
"es": "es_default",
"fr": "fr_default",
"ja": "jp_default",
"ko": "kr_default"
}
output_dir = 'outputs'
os.makedirs(output_dir, exist_ok=True)
def audio_to_base64(audio_file):
with open(audio_file, "rb") as audio_file:
audio_data = audio_file.read()
base64_data = base64.b64encode(audio_data).decode("utf-8")
return base64_data
def count_chars_words(sentence):
segments = re.findall(r'[\u4e00-\u9fa5]+|\w+', sentence)
char_count = 0
word_count = 0
for segment in segments:
if re.match(r'[\u4e00-\u9fa5]+', segment):
char_count += len(segment)
else:
word_count += len(segment.split())
return char_count + word_count
def predict(prompt, style, audio_file_pth, speed, agree):
# initialize a empty info
text_hint = ''
# agree with the terms
if agree == False:
text_hint += '[ERROR] Please accept the Terms & Condition!\n'
gr.Warning("Please accept the Terms & Condition!")
return (
text_hint,
None,
None,
)
# Before we get into inference, we will detect if it is from example table or default value
# If so, we use a cached Audio. Noted that, it is just for demo efficiency.
# hash code were generated by `hash_code_for_cached_output.py`
# this hash get from gradio console
cached_outputs = {
"af39e1f1ff_60565a5c20_en_us" : "cached_outputs/0.wav",
"af39e1f1ff_420ab8211d_en_us" : "cached_outputs/1.wav",
"ced034cc22_0f96bf44f5_es_default" : "cached_outputs/2.wav",
"d3172b178d_3fef5adc6f_zh_default" : "cached_outputs/3.wav",
"cda6998e1a_9897b60a4e_jp_default" : "cached_outputs/4.wav"
}
unique_code = hash_code_for_cached_output.get_unique_code(audio_file_pth, prompt, style)
print("audio_file_pth is", audio_file_pth)
print("unique_code is", unique_code)
if unique_code in list(cached_outputs.keys()):
return (
'We get the cached output for you, since you are trying to generate an example cloning.',
cached_outputs[unique_code],
audio_file_pth,
)
# first detect the input language
language_predicted = langid.classify(prompt)[0].strip()
print(f"Detected language:{language_predicted}")
if language_predicted not in supported_languages:
text_hint += f"[ERROR] The detected language {language_predicted} for your input text is not in our Supported Languages: {supported_languages}\n"
gr.Warning(
f"The detected language {language_predicted} for your input text is not in our Supported Languages: {supported_languages}"
)
return (
text_hint,
None,
None,
)
# check the style
if style not in supported_styles[language_predicted]:
text_hint += f"[Warming] The style {style} is not supported for detected language {language_predicted}. For language {language_predicted}, we support styles: {supported_styles[language_predicted]}. Using the wrong style may result in unexpected behavior.\n"
gr.Warning(f"[Warming] The style {style} is not supported for detected language {language_predicted}. For language {language_predicted}, we support styles: {supported_styles[language_predicted]}. Using the wrong style may result in unexpected behavior.")
prompt_length = count_chars_words(prompt)
speaker_wav = audio_file_pth
if prompt_length < 2:
text_hint += f"[ERROR] Please give a longer prompt text \n"
gr.Warning("Please give a longer prompt text")
return (
text_hint,
None,
None,
)
if prompt_length > 50:
text_hint += f"[ERROR] Text length limited to 50 words for this demo, please try shorter text. You can clone our open-source repo or try it on our website https://app.myshell.ai/robot-workshop/widget/174760057433406749 \n"
gr.Warning(
"Text length limited to 50 words for this demo, please try shorter text. You can clone our open-source repo or try it on our website https://app.myshell.ai/robot-workshop/widget/174760057433406749"
)
return (
text_hint,
None,
None,
)
save_path = f'{output_dir}/output.wav'
speaker_audio_base64 = audio_to_base64(speaker_wav)
if style == 'en_us': # we update us accent
style = 'en_newest'
data = {
"text": prompt,
"reference_speaker": speaker_audio_base64,
"language": style,
"speed": speed
}
start = time.time()
# Send the data as a POST request
response = requests.post(API_URL, json=data, timeout=60)
print(f'Get response successfully within {time.time() - start}')
# Check the response
if response.status_code == 200:
try:
json_data = json.loads(response.content)
text_hint += f"[ERROR] {json_data['error']} \n"
gr.Warning(
f"[ERROR] {json_data['error']} \n"
)
return (
text_hint,
None,
None,
)
except:
with open(save_path, 'wb') as f:
f.write(response.content)
else:
text_hint += f"[HTTP ERROR] {response.status_code} - {response.text} \n"
gr.Warning(
f"[HTTP ERROR] {response.status_code} - {response.text} \n"
)
return (
text_hint,
None,
None,
)
text_hint += f'''Get response successfully \n'''
return (
text_hint,
save_path,
speaker_wav,
)
title = "MyShell OpenVoice V2"
description = """
In December 2023, we released [OpenVoice V1](https://huggingface.co/spaces/myshell-ai/OpenVoice), an instant voice cloning approach that replicates a speaker's voice and generates speech in multiple languages using only a short audio clip. OpenVoice V1 enables granular control over voice styles, replicates the tone color of the reference speaker and achieves zero-shot cross-lingual voice cloning.
"""
description_v2 = """
In April 2024, we released **OpenVoice V2**, which includes all features in V1 and has:
- **Better Audio Quality**. OpenVoice V2 adopts a different training strategy that delivers better audio quality.
- **Native Multi-lingual Support**. English, Spanish, French, Chinese, Japanese and Korean are natively supported in OpenVoice V2.
- **Free Commercial Use**. Starting from April 2024, both V2 and V1 are released under MIT License. Free for commercial use.
"""
markdown_table = """
<div align="center" style="margin-bottom: 10px;">
| | | |
| :-----------: | :-----------: | :-----------: |
| **OpenSource Repo** | **Project Page** | **Join the Community** |
| <div style='text-align: center;'><a style="display:inline-block,align:center" href='https://github.com/myshell-ai/OpenVoice'><img src='https://img.shields.io/github/stars/myshell-ai/OpenVoice?style=social' /></a></div> | [OpenVoice](https://research.myshell.ai/open-voice) | [](https://discord.gg/myshell) |
</div>
"""
markdown_table_v2 = """
<div align="center" style="margin-bottom: 2px;">
| | | | |
| :-----------: | :-----------: | :-----------: | :-----------: |
| **Github Repo** | <div style='text-align: center;'><a style="display:inline-block,align:center" href='https://github.com/myshell-ai/OpenVoice'><img src='https://img.shields.io/github/stars/myshell-ai/OpenVoice?style=social' /></a></div> | **Project Page** | [OpenVoice](https://research.myshell.ai/open-voice) |
| | |
| :-----------: | :-----------: |
**Join the Community** | [](https://discord.gg/myshell) |
</div>
"""
content = """
<div>
<strong>If the generated voice does not sound like the reference voice, please refer to <a href='https://github.com/myshell-ai/OpenVoice/blob/main/docs/QA.md'>this QnA</a>.</strong> <strong>If you want to deploy the model by yourself and perform inference, please refer to <a href='https://github.com/myshell-ai/OpenVoice/blob/main/demo_part3.ipynb'>this jupyter notebook</a>.</strong>
</div>
"""
wrapped_markdown_content = f"<div style='border: 1px solid #000; padding: 10px;'>{content}</div>"
examples = [
[
"Did you ever hear a folk tale about a giant turtle?",
'en_us',
"examples/speaker0.mp3",
True,
],[
"El resplandor del sol acaricia las olas, pintando el cielo con una paleta deslumbrante.",
'es_default',
"examples/speaker1.mp3",
True,
],[
"我最近在学习machine learning，希望能够在未来的artificial intelligence领域有所建树。",
'zh_default',
"examples/speaker2.mp3",
True,
],[
"彼は毎朝ジョギングをして体を健康に保っています。",
'jp_default',
"examples/speaker3.mp3",
True,
],
]
with gr.Blocks(analytics_enabled=False) as demo:
with gr.Row():
with gr.Column():
with gr.Row():
gr.Markdown(
"""
## <img src="https://huggingface.co/spaces/myshell-ai/OpenVoice/raw/main/logo.jpg" height="40"/>
"""
)
with gr.Row():
gr.Markdown(markdown_table_v2)
with gr.Row():
gr.Markdown(description)
with gr.Column():
gr.Video('./openvoicev2.mp4', autoplay=True)
with gr.Row():
gr.Markdown(description_v2)
with gr.Row():
gr.HTML(wrapped_markdown_content)
with gr.Row():
with gr.Column():
input_text_gr = gr.Textbox(
label="Text Prompt",
info="One or two sentences at a time is better. Up to 200 text characters.",
value="The bustling city square bustled with street performers, tourists, and local vendors.",
)
style_gr = gr.Dropdown(
label="Style",
info="Select a style of output audio for the synthesised speech. (Chinese only support 'default' now)",
choices=["en_default", "en_us", "en_br", "en_au", "en_in", "es_default", "fr_default", "jp_default", "zh_default", "kr_default",],
max_choices=1,
value="en_us",
)
ref_gr = gr.Audio(
label="Reference Audio",
info="Click on the ✎ button to upload your own target speaker audio",
type="filepath",
value="examples/speaker0.mp3",
)
tos_gr = gr.Checkbox(
label="Agree",
value=False,
info="I agree to the terms of the MIT license-: https://github.com/myshell-ai/OpenVoice/blob/main/LICENSE",
)
tts_button = gr.Button("Send", elem_id="send-btn", visible=True)
with gr.Column():
out_text_gr = gr.Text(label="Info")
audio_gr = gr.Audio(label="Synthesised Audio", autoplay=True)
ref_audio_gr = gr.Audio(label="Reference Audio Used")
gr.Examples(examples,
label="Examples",
inputs=[input_text_gr, style_gr, ref_gr, tos_gr],
outputs=[out_text_gr, audio_gr, ref_audio_gr],
fn=predict,
cache_examples=False,)
tts_button.click(predict, [input_text_gr, style_gr, ref_gr, tos_gr], outputs=[out_text_gr, audio_gr, ref_audio_gr])
demo.queue(concurrency_count=6)
demo.launch(debug=True, show_api=True)