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whisper-Base
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#!/usr/local/bin/python3
#-*- coding:utf-8 -*-
import gradio as gr
import librosa
import torch
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq

checkpoint = "openai/whisper-large-v2"
processor = AutoProcessor.from_pretrained(checkpoint)
model = AutoModelForSpeechSeq2Seq.from_pretrained(checkpoint)


def process_audio(sampling_rate, waveform):
    # convert from int16 to floating point
    waveform = waveform / 32678.0

    # convert to mono if stereo
    if len(waveform.shape) > 1:
        waveform = librosa.to_mono(waveform.T)

    # resample to 16 kHz if necessary
    if sampling_rate != 16000:
        waveform = librosa.resample(waveform, orig_sr=sampling_rate, target_sr=16000)

    # limit to 30 seconds
    waveform = waveform[:16000*30]

    # make PyTorch tensor
    waveform = torch.tensor(waveform)
    return waveform


def predict(audio, mic_audio=None):
    # audio = tuple (sample_rate, frames) or (sample_rate, (frames, channels))
    if mic_audio is not None:
        sampling_rate, waveform = mic_audio
    elif audio is not None:
        sampling_rate, waveform = audio
    else:
        return "(please provide audio)"

    waveform = process_audio(sampling_rate, waveform)
    inputs = processor(audio=waveform, sampling_rate=16000, return_tensors="pt")
    predicted_ids = model.generate(**inputs, max_length=400)
    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
    return transcription[0]


title = "OpenAI Whisper Large v2"

description = """
本例用于演示 <b>openai/whisper-large-v2</b> 模型的语音识别（ASR）能力。目前没有对模型做微调，基于原始模型开发。 Whisper原始模型主要支持英语语音的识别。英语的效果最好，中文语音识别后只会输出汉语拼音。

<b>更多的信息请参考:</b> <a href="https://huggingface.co/openai/whisper-large-v2">openai/whisper-large-v2</a>。

<b>使用方法:</b> 上传一个音频文件或直接在页面中录制音频。音频会在传递到模型之前转换为单声道并重新采样为16 kHz。
"""

article = """
<div style='margin:20px auto;'>

<p>
    参考:  
    <a href="https://huggingface.co/openai/whisper-large-v2">OpenAI Whisper Large v2</a> |
    <a href="https://github.com/innev">Innev GitHub</a> 
</p>

<p>音频案例:<p>
<ul>
<li>"春日阳光普照大地，正是踏春好时节" 来源: 知琪(Zhiqi)
<li>"Hmm, I don't know" 来源: <a href="https://freesound.org/people/InspectorJ/sounds/519189/">InspectorJ</a> (CC BY 4.0 license)
<li>"Henry V" excerpt 来源: <a href="https://freesound.org/people/acclivity/sounds/24096/">acclivity</a> (CC BY-NC 4.0 license)
<li>"You can see it in the eyes" 来源: <a href="https://freesound.org/people/JoyOhJoy/sounds/165348/">JoyOhJoy</a> (CC0 license)
<li>"We yearn for time" 来源: <a href="https://freesound.org/people/Sample_Me/sounds/610529/">Sample_Me</a> (CC0 license)
</ul>
</div>
"""

examples = [
    ["examples/zhiqi.wav", None],
    ["examples/hmm_i_dont_know.wav", None],
    ["examples/henry5.mp3", None],
    ["examples/yearn_for_time.mp3", None],
    ["examples/see_in_eyes.wav", None],
]

gr.Interface(
    fn=predict,
    inputs=[
        gr.Audio(label="上传语音", source="upload", type="numpy"),
        gr.Audio(label="录制语音", source="microphone", type="numpy"),
    ],
    outputs=[
        gr.Text(label="识别出的文字"),
    ],
    title=title,
    description=description,
    article=article,
    examples=examples,
).launch()