Spaces:
Running
Running
File size: 3,272 Bytes
cf44123 55f7ace cf44123 55f7ace cf44123 eb2c63f cf44123 eb2c63f cf44123 eb2c63f cf44123 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 |
#! /usr/bin/env -S uv run
# /// script
# requires-python = ">=3.10"
# dependencies = ["yt-dlp", "whisper-cpp-pybind", "faster-whisper", "gradio"]
# ///
# dependencies = ["yt-dlp", "mlx-whisper", "sherpa-onnx"]
# dependencies = ["yt-dlp", "soundfile", "pywhispercpp", "mlx-whisper"]
from pathlib import Path
# import mlx_whisper
import yt_dlp
from faster_whisper import WhisperModel
# from pywhispercpp.model import Model as whisper_cpp
# from whispercpp import Whisper
def download(url):
ydl_opts = {
"format": "m4a/bestaudio/best",
"postprocessors": [
{
"key": "FFmpegExtractAudio",
"preferredcodec": "wav",
}
],
"outtmpl": "%(id)s.%(ext)s",
"postprocessor_args": [
"-ar",
"16000",
"-ac",
"1",
],
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl:
error_code = ydl.download([url])
if error_code != 0:
raise Exception("Download failed")
info = ydl.extract_info(url, download=False)
# print(info["id"])
return info["id"] + ".wav"
'''
def asr_mlx(wav_file):
result = mlx_whisper.transcribe(
wav_file,
path_or_hf_repo="mlx-community/whisper-large-v3-turbo",
)
for seg in result["segments"]:
print(seg["text"])
def asr_cpp(wav_file):
from whisper_cpp import Whisper
whisper = Whisper("/home/ubuntu/code/whisper.cpp/models/ggml-large-v3-turbo.bin")
# print(wav_file)
whisper.transcribe(wav_file, diarize=True)
whisper.output(output_csv=True, output_jsn=True, output_lrc=True, output_srt=True, output_txt=True, output_vtt=True, log_score=True)
'''
def asr_fast(wav_file):
model = WhisperModel('large-v3-turbo', device="cpu", compute_type="float32")
# model = Whisper.from_pretrained(
# "ggml-large-v3-turbo.bin",
# basedir="/home/ubuntu/code/whisper.cpp/models/", # for dev
# )
segments, info = model.transcribe(
wav_file, language="zh", initial_prompt="以下是普通话的句子。"
)
# print(info)
# result = [s.text for s in segments if '请不吝点赞' not in s.text]
result = [s.text for s in segments]
print(result)
return '\n'.join(result)
import gradio as gr
def handleURL(url):
f = download(url)
return asr_fast(f)
# return "Hello, " + name + "!" * int(intensity)
# demo = gr.Interface( fn=handleURL, inputs=["text"], outputs=["text"])
with gr.Blocks() as demo:
gr.Markdown(
"""
# fetch video from url, and transcibe to text
---
running very poor server with performance, maybe 1.5x time cost with orignal video processing.
try yourself locally if your have macbook with silicon or high performance GPU.
<https://gist.github.com/yanyaoer/5cc7b0dd6729f306ad3cb740d501cabd#file-0-video2text-py>
""")
name = gr.Textbox(label="video url for transcibe")
output = gr.Textbox(label="Output")
greet_btn = gr.Button("submit")
greet_btn.click(fn=handleURL, inputs=name, outputs=output)
if __name__ == "__main__":
import sys
u = (
sys.argv[1] if len(sys.argv) > 1 else "https://www.bilibili.com/video/BV1ZMNrejEnH/"
)
# w = download(u)
# w = "/tmp/BV1ZMNrejEnH.wav"
# w = "/tmp/1746897004.wav"
# w = "BV1ZMNrejEnH.wav"
# w = "abc.wav"
# asr_fast(w)
# asr_mlx(w)
# print(r)
demo.launch(share=True)
|