up

app.py

@@ -1 +1 @@
-run_demo_low_api_openai.py
+run_demo_ct2.py

requirements.txt

@@ -1,5 +1,6 @@
-git+https://github.com/huggingface/transformers
+git+https://github.com/huggingface/transformers.git
 git+https://github.com/openai/whisper.git
+git+https://github.com/guillaumekln/faster-whisper.git
 nltk
 pandas
 psutil

run_demo_ct2.py

@@ -0,0 +1,324 @@
+#! /usr/bin/env python
+# coding=utf-8
+# Copyright 2022 Bofeng Huang
+
+import datetime
+import logging
+import os
+import re
+import warnings
+
+import gradio as gr
+import pandas as pd
+import psutil
+import pytube as pt
+import torch
+# import whisper
+from faster_whisper import WhisperModel
+from huggingface_hub import hf_hub_download, snapshot_download
+from transformers.utils.logging import disable_progress_bar
+
+import nltk
+nltk.download("punkt")
+
+from nltk.tokenize import sent_tokenize
+
+warnings.filterwarnings("ignore")
+disable_progress_bar()
+
+# DEFAULT_MODEL_NAME = "bofenghuang/whisper-large-v2-cv11-german"
+DEFAULT_MODEL_NAME = "bofenghuang/whisper-large-v2-cv11-german-ct2"
+# CHECKPOINT_FILENAME = "checkpoint_openai.pt"
+
+GEN_KWARGS = {
+    "task": "transcribe",
+    "language": "de",
+    # "without_timestamps": True,
+    # decode options
+    # "beam_size": 5,
+    # "patience": 2,
+    # disable fallback
+    # "compression_ratio_threshold": None,
+    # "logprob_threshold": None,
+    # vad threshold
+    # "no_speech_threshold": None,
+}
+
+logging.basicConfig(
+    format="%(asctime)s [%(levelname)s] [%(name)s] %(message)s",
+    datefmt="%Y-%m-%dT%H:%M:%SZ",
+)
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.DEBUG)
+
+# device = 0 if torch.cuda.is_available() else "cpu"
+# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+logger.info(f"Model will be loaded on device `{device}`")
+
+cached_models = {}
+
+
+def format_timestamp(seconds):
+    return str(datetime.timedelta(seconds=round(seconds)))
+
+
+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_audio_from_youtube(yt_url, downloaded_filename="audio.wav"):
+    yt = pt.YouTube(yt_url)
+    stream = yt.streams.filter(only_audio=True)[0]
+    # stream.download(filename="audio.mp3")
+    stream.download(filename=downloaded_filename)
+    return downloaded_filename
+
+
+def download_video_from_youtube(yt_url, downloaded_filename="video.mp4"):
+    yt = pt.YouTube(yt_url)
+    stream = yt.streams.filter(progressive=True, file_extension="mp4").order_by("resolution").desc().first()
+    stream.download(filename=downloaded_filename)
+    logger.info(f"Download YouTube video from {yt_url}")
+    return downloaded_filename
+
+
+def _print_memory_info():
+    memory = psutil.virtual_memory()
+    logger.info(
+        f"Memory info - Free: {memory.available / (1024 ** 3):.2f} Gb, used: {memory.percent}%, total: {memory.total / (1024 ** 3):.2f} Gb"
+    )
+
+
+def _print_cuda_memory_info():
+    used_mem, tot_mem = torch.cuda.mem_get_info()
+    logger.info(
+        f"CUDA memory info - Free: {used_mem / 1024 ** 3:.2f} Gb, used: {(tot_mem - used_mem) / 1024 ** 3:.2f} Gb, total: {tot_mem / 1024 ** 3:.2f} Gb"
+    )
+
+
+def print_memory_info():
+    _print_memory_info()
+    _print_cuda_memory_info()
+
+
+def maybe_load_cached_pipeline(model_name):
+    model = cached_models.get(model_name)
+    if model is None:
+        # downloaded_model_path = hf_hub_download(repo_id=model_name, filename=CHECKPOINT_FILENAME)
+        downloaded_model_path = snapshot_download(repo_id=model_name)
+
+        # model = whisper.load_model(downloaded_model_path, device=device)
+        model = WhisperModel(downloaded_model_path, device=device, compute_type="float16")
+        logger.info(f"`{model_name}` has been loaded on device `{device}`")
+
+        print_memory_info()
+
+        cached_models[model_name] = model
+    return model
+
+
+def infer(model, filename, with_timestamps, return_df=False):
+    if with_timestamps:
+        # model_outputs = model.transcribe(filename, **GEN_KWARGS)
+        model_outputs, _ = model.transcribe(filename, **GEN_KWARGS)
+        model_outputs = [segment._asdict() for segment in model_outputs]
+        if return_df:
+            # model_outputs_df = pd.DataFrame(model_outputs["segments"])
+            model_outputs_df = pd.DataFrame(model_outputs)
+            # print(model_outputs)
+            # print(model_outputs_df)
+            # print(model_outputs_df.info(verbose=True))
+            model_outputs_df = model_outputs_df[["start", "end", "text"]]
+            model_outputs_df["start"] = model_outputs_df["start"].map(format_timestamp)
+            model_outputs_df["end"] = model_outputs_df["end"].map(format_timestamp)
+            model_outputs_df["text"] = model_outputs_df["text"].str.strip()
+            return model_outputs_df
+        else:
+            return "\n\n".join(
+                [
+                    f'Segment {segment["id"]+1} from {segment["start"]:.2f}s to {segment["end"]:.2f}s:\n{segment["text"].strip()}'
+                    # for segment in model_outputs["segments"]
+                    for segment in model_outputs
+                ]
+            )
+    else:
+        # text = model.transcribe(filename, without_timestamps=True, **GEN_KWARGS)["text"]
+        model_outputs, _ = model.transcribe(filename, without_timestamps=True, **GEN_KWARGS)
+        text = " ".join([segment.text for segment in model_outputs])
+        if return_df:
+            return pd.DataFrame({"text": sent_tokenize(text)})
+        else:
+            return text
+
+
+def transcribe(microphone, file_upload, with_timestamps, model_name=DEFAULT_MODEL_NAME):
+    warn_output = ""
+    if (microphone is not None) and (file_upload is not None):
+        warn_output = (
+            "WARNING: You've uploaded an audio file and used the microphone. "
+            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+        )
+
+    elif (microphone is None) and (file_upload is None):
+        return "ERROR: You have to either use the microphone or upload an audio file"
+
+    file = microphone if microphone is not None else file_upload
+
+    model = maybe_load_cached_pipeline(model_name)
+    # text = model.transcribe(file, **GEN_KWARGS)["text"]
+    # text = infer(model, file, with_timestamps)
+    text = infer(model, file, with_timestamps, return_df=True)
+
+    logger.info(f'Transcription by `{model_name}`:\n{text.to_json(orient="index", force_ascii=False, indent=2)}\n')
+
+    # return warn_output + text
+    return text
+
+
+def yt_transcribe(yt_url, with_timestamps, model_name=DEFAULT_MODEL_NAME):
+    # html_embed_str = _return_yt_html_embed(yt_url)
+    audio_file_path = download_audio_from_youtube(yt_url)
+
+    model = maybe_load_cached_pipeline(model_name)
+    # text = model.transcribe("audio.mp3", **GEN_KWARGS)["text"]
+    # text = infer(model, audio_file_path, with_timestamps)
+    text = infer(model, audio_file_path, with_timestamps, return_df=True)
+
+    logger.info(f'Transcription by `{model_name}` of "{yt_url}":\n{text.to_json(orient="index", force_ascii=False, indent=2)}\n')
+
+    # return html_embed_str, text
+    return text
+
+
+def video_transcribe(video_file_path, with_timestamps, model_name=DEFAULT_MODEL_NAME):
+    if video_file_path is None:
+        raise ValueError("Failed to transcribe video as no video_file_path has been defined")
+
+    audio_file_path = re.sub(r"\.mp4$", ".wav", video_file_path)
+    os.system(f'ffmpeg -hide_banner -loglevel error -y -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{audio_file_path}"')
+
+    model = maybe_load_cached_pipeline(model_name)
+    # text = model.transcribe("audio.mp3", **GEN_KWARGS)["text"]
+    text = infer(model, audio_file_path, with_timestamps, return_df=True)
+
+    logger.info(f'Transcription by `{model_name}`:\n{text.to_json(orient="index", force_ascii=False, indent=2)}\n')
+
+    return text
+
+
+# load default model
+maybe_load_cached_pipeline(DEFAULT_MODEL_NAME)
+
+# default_text_output_df = pd.DataFrame(columns=["start", "end", "text"])
+default_text_output_df = pd.DataFrame(columns=["text"])
+
+with gr.Blocks() as demo:
+
+    with gr.Tab("Transcribe Audio"):
+        gr.Markdown(
+            f"""
+              <div>
+              <h1 style='text-align: center'>Whisper German Demo: Transcribe Audio</h1>
+              </div>
+              Transcribe long-form microphone or audio inputs!
+
+              Demo uses the fine-tuned checkpoint: <a href='https://huggingface.co/{DEFAULT_MODEL_NAME}' target='_blank'><b>{DEFAULT_MODEL_NAME}</b></a> to transcribe audio files of arbitrary length.
+
+              Efficient inference is supported by [faster-whisper](https://github.com/guillaumekln/faster-whisper) and [CTranslate2](https://github.com/OpenNMT/CTranslate2).
+          """
+        )
+
+        microphone_input = gr.inputs.Audio(source="microphone", type="filepath", label="Record", optional=True)
+        upload_input = gr.inputs.Audio(source="upload", type="filepath", label="Upload File", optional=True)
+        with_timestamps_input = gr.Checkbox(label="With timestamps?")
+
+        microphone_transcribe_btn = gr.Button("Transcribe Audio")
+
+        # gr.Markdown('''
+        #     Here you will get generated transcrit.
+        # ''')
+
+        # microphone_text_output = gr.outputs.Textbox(label="Transcription")
+        text_output_df2 = gr.DataFrame(
+            value=default_text_output_df,
+            label="Transcription",
+            row_count=(0, "dynamic"),
+            max_rows=10,
+            wrap=True,
+            overflow_row_behaviour="paginate",
+        )
+
+        microphone_transcribe_btn.click(
+            transcribe, inputs=[microphone_input, upload_input, with_timestamps_input], outputs=text_output_df2
+        )
+
+    # with gr.Tab("Transcribe YouTube"):
+    #     gr.Markdown(
+    #         f"""
+    #           <div>
+    #           <h1 style='text-align: center'>Whisper German Demo: Transcribe YouTube</h1>
+    #           </div>
+    #           Transcribe long-form YouTube videos!
+
+    #           Demo uses the fine-tuned checkpoint: <a href='https://huggingface.co/{DEFAULT_MODEL_NAME}' target='_blank'><b>{DEFAULT_MODEL_NAME}</b></a> to transcribe video files of arbitrary length.
+    #       """
+    #     )
+
+    #     yt_link_input2 = gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")
+    #     with_timestamps_input2 = gr.Checkbox(label="With timestamps?", value=True)
+
+    #     yt_transcribe_btn = gr.Button("Transcribe YouTube")
+
+    #     # yt_text_output = gr.outputs.Textbox(label="Transcription")
+    #     text_output_df3 = gr.DataFrame(
+    #         value=default_text_output_df,
+    #         label="Transcription",
+    #         row_count=(0, "dynamic"),
+    #         max_rows=10,
+    #         wrap=True,
+    #         overflow_row_behaviour="paginate",
+    #     )
+    #     # yt_html_output = gr.outputs.HTML(label="YouTube Page")
+
+    #     yt_transcribe_btn.click(yt_transcribe, inputs=[yt_link_input2, with_timestamps_input2], outputs=[text_output_df3])
+
+    with gr.Tab("Transcribe Video"):
+        gr.Markdown(
+            f"""
+              <div>
+              <h1 style='text-align: center'>Whisper German Demo: Transcribe Video</h1>
+              </div>
+              Transcribe long-form YouTube videos or uploaded video inputs!
+
+              Demo uses the fine-tuned checkpoint: <a href='https://huggingface.co/{DEFAULT_MODEL_NAME}' target='_blank'><b>{DEFAULT_MODEL_NAME}</b></a> to transcribe video files of arbitrary length.
+
+              Efficient inference is supported by [faster-whisper](https://github.com/guillaumekln/faster-whisper) and [CTranslate2](https://github.com/OpenNMT/CTranslate2).
+          """
+        )
+
+        yt_link_input = gr.inputs.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")
+        download_youtube_btn = gr.Button("Download Youtube video")
+        downloaded_video_output = gr.Video(label="Video file", mirror_webcam=False)
+        download_youtube_btn.click(download_video_from_youtube, inputs=[yt_link_input], outputs=[downloaded_video_output])
+
+        with_timestamps_input3 = gr.Checkbox(label="With timestamps?", value=True)
+        video_transcribe_btn = gr.Button("Transcribe video")
+        text_output_df = gr.DataFrame(
+            value=default_text_output_df,
+            label="Transcription",
+            row_count=(0, "dynamic"),
+            max_rows=10,
+            wrap=True,
+            overflow_row_behaviour="paginate",
+        )
+
+        video_transcribe_btn.click(video_transcribe, inputs=[downloaded_video_output, with_timestamps_input3], outputs=[text_output_df])
+
+# demo.launch(server_name="0.0.0.0", debug=True)
+# demo.launch(server_name="0.0.0.0", debug=True, share=True)
+demo.launch(enable_queue=True)