import spaces import torch import gradio as gr import yt_dlp as youtube_dl from pytubefix import YouTube from pytubefix.cli import on_progress from transformers import pipeline from transformers.pipelines.audio_utils import ffmpeg_read import tempfile import os MODEL_NAME = "razhan/whisper-base-hawrami-transcription" BATCH_SIZE = 1 FILE_LIMIT_MB = 30 YT_LENGTH_LIMIT_S = 60 * 10 # limit to 1 hour YouTube files device = 0 if torch.cuda.is_available() else "cpu" pipe = pipeline( task="automatic-speech-recognition", model=MODEL_NAME, chunk_length_s=30, device=device, ) # @spaces.GPU def transcribe(inputs, task="transcribe"): if inputs is None: raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.") text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] return text def _return_yt_html_embed(yt_url): video_id = yt_url.split("?v=")[-1] HTML_str = ( f'
' "
" ) return HTML_str # def download_yt_audio(yt_url, filename): # info_loader = youtube_dl.YoutubeDL() # try: # info = info_loader.extract_info(yt_url, download=False) # except youtube_dl.utils.DownloadError as err: # raise gr.Error(str(err)) # file_length = info["duration_string"] # file_h_m_s = file_length.split(":") # file_h_m_s = [int(sub_length) for sub_length in file_h_m_s] # if len(file_h_m_s) == 1: # file_h_m_s.insert(0, 0) # if len(file_h_m_s) == 2: # file_h_m_s.insert(0, 0) # file_length_s = file_h_m_s[0] * 3600 + file_h_m_s[1] * 60 + file_h_m_s[2] # if file_length_s > YT_LENGTH_LIMIT_S: # yt_length_limit_hms = time.strftime("%HH:%MM:%SS", time.gmtime(YT_LENGTH_LIMIT_S)) # file_length_hms = time.strftime("%HH:%MM:%SS", time.gmtime(file_length_s)) # raise gr.Error(f"Maximum YouTube length is {yt_length_limit_hms}, got {file_length_hms} YouTube video.") # ydl_opts = {"outtmpl": filename, "format": "worstvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best"} # with youtube_dl.YoutubeDL(ydl_opts) as ydl: # try: # ydl.download([yt_url]) # except youtube_dl.utils.ExtractorError as err: # raise gr.Error(str(err)) # yt = pt.YouTube(yt_url) # stream = yt.streams.filter(only_audio=True)[0] # stream.download(filename=filename) # @spaces.GPU # def yt_transcribe(yt_url, task="transcribe", max_filesize=75.0): # html_embed_str = _return_yt_html_embed(yt_url) # with tempfile.TemporaryDirectory() as tmpdirname: # # filepath = os.path.join(tmpdirname, "video.mp4") # filepath = os.path.join(tmpdirname, "audio.mp3") # download_yt_audio(yt_url, filepath) # with open(filepath, "rb") as f: # inputs = f.read() # inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate) # inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate} # text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] # return html_embed_str, text def yt_transcribe(yt_url, task="transcribe", progress=gr.Progress(), max_filesize=75.0): progress(0, desc="Loading audio file...") html_embed_str = _return_yt_html_embed(yt_url) try: # yt = pytube.YouTube(yt_url) # stream = yt.streams.filter(only_audio=True)[0] yt = YouTube(yt_url, on_progress_callback = on_progress, use_po_token=True) stream = yt.streams.get_audio_only() except: raise gr.Error("An error occurred while loading the YouTube video. Please try again.") if stream.filesize_mb > max_filesize: raise gr.Error(f"Maximum YouTube file size is {max_filesize}MB, got {stream.filesize_mb:.2f}MB.") # stream.download(filename="audio.mp3") stream.download(filename="audio.mp3", mp3=True) with open("audio.mp3", "rb") as f: inputs = f.read() inputs = ffmpeg_read(inputs, pipe.feature_extractor.sampling_rate) inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate} text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)["text"] return html_embed_str, text demo = gr.Blocks(theme=gr.themes.Ocean()) mf_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.Audio(sources="microphone", type="filepath"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), ], outputs="text", title="Whisper Horami Demo: Transcribe Audio", description=( "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME}](https://huggingface.co./{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" " of arbitrary length." ), flagging_mode="never", ) file_transcribe = gr.Interface( fn=transcribe, inputs=[ gr.Audio(sources="upload", type="filepath", label="Audio file"), gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), ], outputs="text", title="Whisper Horami Demo: Transcribe Audio", description=( "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the" f" checkpoint [{MODEL_NAME}](https://huggingface.co./{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" " of arbitrary length." ), flagging_mode="never", ) yt_transcribe = gr.Interface( fn=yt_transcribe, inputs=[ gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL"), # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe") ], outputs=["html", "text"], title="Whisper Horami Demo: Translate YouTube", description=( "Transcribe long-form YouTube videos with the click of a button! Demo uses the checkpoint" f" [{MODEL_NAME}](https://huggingface.co./{MODEL_NAME}) and 🤗 Transformers to transcribe video files of" " arbitrary length." ), flagging_mode="never", ) with demo: # gr.TabbedInterface([mf_transcribe, file_transcribe, yt_transcribe], ["Microphone", "Audio file", "YouTube"]) gr.TabbedInterface([mf_transcribe, file_transcribe], ["Microphone", "Audio file"]) demo.queue().launch(ssr_mode=False) # import spaces # import torch # import gradio as gr # from pytubefix import YouTube # from pytubefix.cli import on_progress # from transformers import pipeline # from transformers.pipelines.audio_utils import ffmpeg_read # import tempfile # import os # MODEL_NAME = "razhan/whisper-base-hawrami-transcription" # BATCH_SIZE = 1 # device = 0 if torch.cuda.is_available() else "cpu" # pipe = pipeline( # task="automatic-speech-recognition", # model=MODEL_NAME, # chunk_length_s=30, # device=device, # ) # def transcribe(inputs, task="transcribe"): # if inputs is None: # raise gr.Error("Please upload or record an audio file before submitting.") # result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True) # return result["text"] # def _return_yt_html_embed(yt_url): # video_id = yt_url.split("?v=")[-1] # return f'
' # def yt_transcribe(yt_url, task="transcribe", progress=gr.Progress()): # progress(0, desc="Loading audio file...") # html_embed = _return_yt_html_embed(yt_url) # try: # yt = YouTube(yt_url, on_progress_callback=on_progress, use_po_token=True) # stream = yt.streams.get_audio_only() # except Exception as e: # raise gr.Error(f"Error loading YouTube video: {str(e)}") # with tempfile.TemporaryDirectory() as tmpdir: # file_path = os.path.join(tmpdir, "audio.mp3") # stream.download(filename=file_path) # with open(file_path, "rb") as f: # audio_data = f.read() # audio = ffmpeg_read(audio_data, pipe.feature_extractor.sampling_rate) # inputs = {"array": audio, "sampling_rate": pipe.feature_extractor.sampling_rate} # result = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True) # return html_embed, result["text"] # demo = gr.Blocks(theme=gr.themes.Ocean()) # common_inputs = [ # gr.Radio(["transcribe", "translate"], label="Task", value="transcribe") # ] # mf_transcribe = gr.Interface( # fn=transcribe, # inputs=[ # gr.Audio(sources="microphone", type="filepath"), # *common_inputs # ], # outputs="text", # title="Whisper Horami: Live Transcription", # description="Transcribe audio from your microphone in real-time" # ) # file_transcribe = gr.Interface( # fn=transcribe, # inputs=[ # gr.Audio(sources="upload", type="filepath", label="Audio file"), # *common_inputs # ], # outputs="text", # title="Whisper Horami: File Transcription", # description="Upload an audio file for transcription" # ) # yt_interface = gr.Interface( # fn=yt_transcribe, # inputs=[ # gr.Textbox(placeholder="YouTube URL", label="Video URL"), # *common_inputs # ], # outputs=["html", "text"], # title="Whisper Horami: YouTube Transcription", # description="Transcribe audio from YouTube videos" # ) # with demo: # gr.TabbedInterface( # [mf_transcribe, file_transcribe], # ["Microphone", "Audio File",] # ) # demo.queue().launch(ssr_mode=False)