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Update app.py
#10
by
Jesuscarr
- opened
app.py
CHANGED
@@ -1,10 +1,8 @@
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import torch
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-
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import gradio as gr
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import yt_dlp as youtube_dl
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from transformers import pipeline
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from transformers.pipelines.audio_utils import ffmpeg_read
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-
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import tempfile
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import os
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@@ -22,7 +20,6 @@ pipe = pipeline(
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device=device,
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)
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-
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def transcribe(microphone, file_upload, task):
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warn_output = ""
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if (microphone is not None) and (file_upload is not None):
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@@ -34,19 +31,18 @@ def transcribe(microphone, file_upload, task):
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elif (microphone is None) and (file_upload is None):
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raise gr.Error("You have to either use the microphone or upload an audio file")
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if file_size_mb > FILE_LIMIT_MB:
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raise gr.Error(
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)
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text = pipe(file, batch_size=BATCH_SIZE, generate_kwargs={"task": task})["text"]
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return warn_output + text
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def _return_yt_html_embed(yt_url):
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video_id = yt_url.split("?v=")[-1]
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HTML_str = (
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@@ -61,18 +57,9 @@ def download_yt_audio(yt_url, filename):
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info = info_loader.extract_info(yt_url, download=False)
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except youtube_dl.utils.DownloadError as err:
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raise gr.Error(str(err))
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file_h_m_s = file_length.split(":")
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file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
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if len(file_h_m_s) == 1:
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file_h_m_s.insert(0, 0)
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if len(file_h_m_s) == 2:
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file_h_m_s.insert(0, 0)
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file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2]
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if file_length_s > YT_LENGTH_LIMIT_S:
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file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s))
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raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.")
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ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
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with youtube_dl.YoutubeDL(ydl_opts) as ydl:
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try:
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@@ -80,7 +67,6 @@ def download_yt_audio(yt_url, filename):
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except youtube_dl.utils.ExtractorError as err:
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raise gr.Error(str(err))
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-
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def yt_transcribe(yt_url, task, max_filesize=75.0):
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html_embed_str = _return_yt_html_embed(yt_url)
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@@ -93,11 +79,10 @@ def yt_transcribe(yt_url, task, max_filesize=75.0):
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inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
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inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
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text = pipe(inputs, batch_size=BATCH_SIZE,
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return html_embed_str, text
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demo = gr.Blocks()
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mf_transcribe = gr.Interface(
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@@ -140,5 +125,4 @@ yt_transcribe = gr.Interface(
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with demo:
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gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
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demo.launch(enable_queue=True)
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-
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import torch
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import gradio as gr
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import yt_dlp as youtube_dl
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from transformers import pipeline
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from transformers.pipelines.audio_utils import ffmpeg_read
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import tempfile
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import os
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device=device,
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)
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def transcribe(microphone, file_upload, task):
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warn_output = ""
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if (microphone is not None) and (file_upload is not None):
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elif (microphone is None) and (file_upload is None):
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raise gr.Error("You have to either use the microphone or upload an audio file")
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file = microphone if microphone is not None else file_upload
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file_size_mb = os.stat(file).st_size / (1024 * 1024)
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if file_size_mb > FILE_LIMIT_MB:
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raise gr.Error(
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f"File size exceeds file size limit. Got file of size {file_size_mb:.2f}MB for a limit of {FILE_LIMIT_MB}MB."
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)
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text = pipe(file, batch_size=BATCH_SIZE,
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generate_kwargs={"task": task})["text"]
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return warn_output + text
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def _return_yt_html_embed(yt_url):
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video_id = yt_url.split("?v=")[-1]
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HTML_str = (
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info = info_loader.extract_info(yt_url, download=False)
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except youtube_dl.utils.DownloadError as err:
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raise gr.Error(str(err))
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file_length_s = info.get('duration', 0)
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if file_length_s > YT_LENGTH_LIMIT_S:
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raise gr.Error(f"Maximum YouTube length is {YT_LENGTH_LIMIT_S} seconds, got {file_length_s} seconds YouTube video.")
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ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"}
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with youtube_dl.YoutubeDL(ydl_opts) as ydl:
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try:
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except youtube_dl.utils.ExtractorError as err:
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raise gr.Error(str(err))
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def yt_transcribe(yt_url, task, max_filesize=75.0):
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html_embed_str = _return_yt_html_embed(yt_url)
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inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate)
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inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
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text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task})["text"]
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return html_embed_str, text
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demo = gr.Blocks()
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mf_transcribe = gr.Interface(
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with demo:
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gr.TabbedInterface([mf_transcribe, yt_transcribe], ["Transcribe Audio", "Transcribe YouTube"])
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demo.launch(enable_queue=True)
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