| import torch | |
| import gradio as gr | |
| from transformers import pipeline | |
| from transformers.pipelines.audio_utils import ffmpeg_read | |
| import tempfile | |
| import os | |
| access_token=os.getenv("access_token") | |
| MODEL_NAME = "ciditel/whisper-large-v3" | |
| BATCH_SIZE = 3 | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| pipe = pipeline( | |
| task="automatic-speech-recognition", | |
| model=MODEL_NAME, | |
| #chunk_length_s=30, | |
| device=device, | |
| token=access_token | |
| ) | |
| def transcribe(inputs, task): | |
| 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})["text"] | |
| return text | |
| #def _return_yt_html_embed(yt_url): | |
| #video_id = yt_url.split("?v=")[-1] | |
| #HTML_str = ( | |
| # f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>' | |
| # " </center>" | |
| #) | |
| #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)) | |
| #def yt_transcribe(yt_url, task, 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") | |
| #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 None#html_embed_str, text | |
| demo = gr.Blocks() | |
| gradio_app = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.Audio(type='filepath'), | |
| gr.Radio(["transcribe", "translate"], label="Task", value="transcribe"), | |
| ], | |
| outputs="text", | |
| #layout="horizontal", | |
| #theme="huggingface", | |
| #title="Whisper Large V3: Transcribe Audio", | |
| #description=( | |
| # "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper" | |
| # f" checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to transcribe audio files" | |
| # " of arbitrary length." | |
| #), | |
| #allow_flagging="never", | |
| ) | |
| if __name__ == "__main__": | |
| gradio_app.launch() | |