EAI-sense-voice/webui.py

# coding=utf-8

import gradio as gr
import numpy as np
import torch
import torchaudio

from funasr import AutoModel

model = "iic/SenseVoiceSmall"
model = AutoModel(model=model,
                  vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                  vad_kwargs={"max_single_segment_time": 30000},
                  trust_remote_code=True,
                  )

emo_dict = {
    "<|HAPPY|>": "😊",
    "<|SAD|>": "😔",
    "<|ANGRY|>": "😡",
    "<|NEUTRAL|>": "",
    "<|FEARFUL|>": "😰",
    "<|DISGUSTED|>": "🤢",
    "<|SURPRISED|>": "😮",
}

event_dict = {
    "<|BGM|>": "🎼",
    "<|Speech|>": "",
    "<|Applause|>": "👏",
    "<|Laughter|>": "😀",
    "<|Cry|>": "😭",
    "<|Sneeze|>": "🤧",
    "<|Breath|>": "",
    "<|Cough|>": "🤧",
}

emoji_dict = {
    "<|nospeech|><|Event_UNK|>": "❓",
    "<|zh|>": "",
    "<|en|>": "",
    "<|yue|>": "",
    "<|ja|>": "",
    "<|ko|>": "",
    "<|nospeech|>": "",
    "<|HAPPY|>": "😊",
    "<|SAD|>": "😔",
    "<|ANGRY|>": "😡",
    "<|NEUTRAL|>": "",
    "<|BGM|>": "🎼",
    "<|Speech|>": "",
    "<|Applause|>": "👏",
    "<|Laughter|>": "😀",
    "<|FEARFUL|>": "😰",
    "<|DISGUSTED|>": "🤢",
    "<|SURPRISED|>": "😮",
    "<|Cry|>": "😭",
    "<|EMO_UNKNOWN|>": "",
    "<|Sneeze|>": "🤧",
    "<|Breath|>": "",
    "<|Cough|>": "😷",
    "<|Sing|>": "",
    "<|Speech_Noise|>": "",
    "<|withitn|>": "",
    "<|woitn|>": "",
    "<|GBG|>": "",
    "<|Event_UNK|>": "",
}

lang_dict = {
    "<|zh|>": "<|lang|>",
    "<|en|>": "<|lang|>",
    "<|yue|>": "<|lang|>",
    "<|ja|>": "<|lang|>",
    "<|ko|>": "<|lang|>",
    "<|nospeech|>": "<|lang|>",
}

emo_set = {"😊", "😔", "😡", "😰", "🤢", "😮"}
event_set = {"🎼", "👏", "😀", "😭", "🤧", "😷", }


def format_str(s):
    for sptk in emoji_dict:
        s = s.replace(sptk, emoji_dict[sptk])
    return s


def format_str_v2(s):
    sptk_dict = {}
    for sptk in emoji_dict:
        sptk_dict[sptk] = s.count(sptk)
        s = s.replace(sptk, "")
    emo = "<|NEUTRAL|>"
    for e in emo_dict:
        if sptk_dict[e] > sptk_dict[emo]:
            emo = e
    for e in event_dict:
        if sptk_dict[e] > 0:
            s = event_dict[e] + s
    s = s + emo_dict[emo]

    for emoji in emo_set.union(event_set):
        s = s.replace(" " + emoji, emoji)
        s = s.replace(emoji + " ", emoji)
    return s.strip()


def format_str_v3(s):
    def get_emo(s):
        return s[-1] if s[-1] in emo_set else None

    def get_event(s):
        return s[0] if s[0] in event_set else None

    s = s.replace("<|nospeech|><|Event_UNK|>", "❓")
    for lang in lang_dict:
        s = s.replace(lang, "<|lang|>")
    s_list = [format_str_v2(s_i).strip(" ") for s_i in s.split("<|lang|>")]
    new_s = " " + s_list[0]
    cur_ent_event = get_event(new_s)
    for i in range(1, len(s_list)):
        if len(s_list[i]) == 0:
            continue
        if get_event(s_list[i]) == cur_ent_event and get_event(s_list[i]) != None:
            s_list[i] = s_list[i][1:]
        # else:
        cur_ent_event = get_event(s_list[i])
        if get_emo(s_list[i]) != None and get_emo(s_list[i]) == get_emo(new_s):
            new_s = new_s[:-1]
        new_s += s_list[i].strip().lstrip()
    new_s = new_s.replace("The.", " ")
    return new_s.strip()


def model_inference(input_wav, language, fs=16000):
    # task_abbr = {"Speech Recognition": "ASR", "Rich Text Transcription": ("ASR", "AED", "SER")}
    language_abbr = {"auto": "auto", "zh": "zh", "en": "en", "yue": "yue", "ja": "ja", "ko": "ko",
                     "nospeech": "nospeech"}

    # task = "Speech Recognition" if task is None else task
    language = "auto" if len(language) < 1 else language
    selected_language = language_abbr[language]
    # selected_task = task_abbr.get(task)

    # print(f"input_wav: {type(input_wav)}, {input_wav[1].shape}, {input_wav}")

    if isinstance(input_wav, tuple):
        fs, input_wav = input_wav
        input_wav = input_wav.astype(np.float32) / np.iinfo(np.int16).max
        if len(input_wav.shape) > 1:
            input_wav = input_wav.mean(-1)
        if fs != 16000:
            print(f"audio_fs: {fs}")
            resampler = torchaudio.transforms.Resample(fs, 16000)
            input_wav_t = torch.from_numpy(input_wav).to(torch.float32)
            input_wav = resampler(input_wav_t[None, :])[0, :].numpy()

    merge_vad = True  # False if selected_task == "ASR" else True
    print(f"language: {language}, merge_vad: {merge_vad}")
    text = model.generate(input=input_wav,
                          cache={},
                          language=language,
                          use_itn=True,
                          batch_size_s=60, merge_vad=merge_vad)

    print(text)
    text = text[0]["text"]
    text = format_str_v3(text)

    print(text)

    return text


audio_examples = [
    ["example/zh.mp3", "zh"],
    ["example/yue.mp3", "yue"],
    ["example/en.mp3", "en"],
    ["example/ja.mp3", "ja"],
    ["example/ko.mp3", "ko"],
    ["example/emo_1.wav", "auto"],
    ["example/emo_2.wav", "auto"],
    ["example/emo_3.wav", "auto"],
	# ["example/emo_4.wav", "auto"],
	# ["example/event_1.wav", "auto"],
	# ["example/event_2.wav", "auto"],
	# ["example/event_3.wav", "auto"],
    ["example/rich_1.wav", "auto"],
    ["example/rich_2.wav", "auto"],
	# ["example/rich_3.wav", "auto"],
    ["example/longwav_1.wav", "auto"],
    ["example/longwav_2.wav", "auto"],
    ["example/longwav_3.wav", "auto"],
	# ["example/longwav_4.wav", "auto"],
]

html_content = """
<div>
    <h2 style="font-size: 22px;margin-left: 0px;">Voice Understanding Model: SenseVoice-Small</h2>
    <p style="font-size: 18px;margin-left: 20px;">SenseVoice-Small is an encoder-only speech foundation model designed for rapid voice understanding. It encompasses a variety of features including automatic speech recognition (ASR), spoken language identification (LID), speech emotion recognition (SER), and acoustic event detection (AED). SenseVoice-Small supports multilingual recognition for Chinese, English, Cantonese, Japanese, and Korean. Additionally, it offers exceptionally low inference latency, performing 7 times faster than Whisper-small and 17 times faster than Whisper-large.</p>
    <h2 style="font-size: 22px;margin-left: 0px;">Usage</h2> <p style="font-size: 18px;margin-left: 20px;">Upload an audio file or input through a microphone, then select the task and language. the audio is transcribed into corresponding text along with associated emotions (😊 happy, 😡 angry/exicting, 😔 sad) and types of sound events (😀 laughter, 🎼 music, 👏 applause, 🤧 cough&sneeze, 😭 cry). The event labels are placed in the front of the text and the emotion are in the back of the text.</p>
	<p style="font-size: 18px;margin-left: 20px;">Recommended audio input duration is below 30 seconds. For audio longer than 30 seconds, local deployment is recommended.</p>
	<h2 style="font-size: 22px;margin-left: 0px;">Repo</h2>
	<p style="font-size: 18px;margin-left: 20px;"><a href="https://github.com/FunAudioLLM/SenseVoice" target="_blank">SenseVoice</a>: multilingual speech understanding model</p>
	<p style="font-size: 18px;margin-left: 20px;"><a href="https://github.com/modelscope/FunASR" target="_blank">FunASR</a>: fundamental speech recognition toolkit</p>
	<p style="font-size: 18px;margin-left: 20px;"><a href="https://github.com/FunAudioLLM/CosyVoice" target="_blank">CosyVoice</a>: high-quality multilingual TTS model</p>
</div>
"""


def launch():
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        # gr.Markdown(description)
        gr.HTML(html_content)
        with gr.Row():
            with gr.Column():
                audio_inputs = gr.Audio(label="Upload audio or use the microphone")

                with gr.Accordion("Configuration"):
                    language_inputs = gr.Dropdown(choices=["auto", "zh", "en", "yue", "ja", "ko", "nospeech"],
                                                  value="auto",
                                                  label="Language")
                fn_button = gr.Button("Start", variant="primary")
                text_outputs = gr.Textbox(label="Results")
            gr.Examples(examples=audio_examples, inputs=[audio_inputs, language_inputs], examples_per_page=20)

        fn_button.click(model_inference, inputs=[audio_inputs, language_inputs], outputs=text_outputs)

    demo.launch()


if __name__ == "__main__":
    # iface.launch()
    launch()