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| import os | |
| import gradio as gr | |
| from transformers import pipeline | |
| from pytube import YouTube | |
| from datasets import Dataset, Audio | |
| from moviepy.editor import AudioFileClip | |
| pipe = pipeline(model="irena/whisper-small-sv-SE") | |
| def download_from_youtube(url): | |
| """ | |
| Downloads the video from the given YouTube URL and returns the path to the audio file. | |
| """ | |
| streams = YouTube(url).streams.filter(only_audio=True, file_extension='mp4') | |
| fpath = streams.first().download() | |
| return fpath | |
| def get_timestamp(seconds): | |
| """ | |
| Creates %M:%S timestamp from seconds. | |
| """ | |
| minutes = int(seconds / 60) | |
| seconds = int(seconds % 60) | |
| return f"{str(minutes).zfill(2)}:{str(seconds).zfill(2)}" | |
| def divide_into_30s_segments(audio_fpath, seconds_max): | |
| """ | |
| Divides the audio file into 30s segments and returns the paths to the segments and the start times of the segments. | |
| :param audio_fpath: Path to the audio file. | |
| :param seconds_max: Maximum number of seconds to consider. If the audio file is longer than this, it will be truncated. | |
| """ | |
| if not os.path.exists("segmented_audios"): | |
| os.makedirs("segmented_audios") | |
| sound = AudioFileClip(audio_fpath) | |
| n_full_segments = int(sound.duration / 30) | |
| len_last_segment = sound.duration % 30 | |
| max_segments = int(seconds_max / 30) | |
| if n_full_segments > max_segments: | |
| n_full_segments = max_segments | |
| len_last_segment = 0 | |
| segment_paths = [] | |
| segment_start_times = [] | |
| segments_available = n_full_segments + 1 | |
| for i in range(min(segments_available, max_segments)): | |
| start = i * 30 | |
| # Skip last segment if it is smaller than two seconds | |
| is_last_segment = i == n_full_segments | |
| if is_last_segment and not len_last_segment > 2: | |
| continue | |
| elif is_last_segment: | |
| end = start + len_last_segment | |
| else: | |
| end = (i + 1) * 30 | |
| segment_path = os.path.join("segmented_audios", f"segment_{i}.wav") | |
| segment = sound.subclip(start, end) | |
| segment.write_audiofile(segment_path) | |
| segment_paths.append(segment_path) | |
| segment_start_times.append(start) | |
| return segment_paths, segment_start_times | |
| def get_translation(text): | |
| """ | |
| Translates the given Chinese text to English. | |
| """ | |
| return "TODO: Make API call to Google Translate to get English translation" | |
| def transcribe(audio, url, seconds_max): | |
| """ | |
| Transcribes a YouTube video if a url is specified and returns the transcription. | |
| If not url is specified, it transcribes the audio file as passed by Gradio. | |
| :param audio: Audio file as passed by Gradio. Only used if no url is specified. | |
| :param url: YouTube URL to transcribe. | |
| :param seconds_max: Maximum number of seconds to consider. If the audio file is longer than this, it will be truncated. | |
| """ | |
| if url: | |
| fpath = download_from_youtube(url) | |
| segment_paths, segment_start_times = divide_into_30s_segments(fpath, seconds_max) | |
| audio_dataset = Dataset.from_dict({"audio": segment_paths}).cast_column("audio", Audio(sampling_rate=16000)) | |
| pred = pipe(audio_dataset["audio"]) | |
| text = "" | |
| n_segments = len(segment_start_times) | |
| for i, (seconds, output) in enumerate(zip(segment_start_times, pred)): | |
| text += f"[Segment {i+1}/{n_segments}, start time {get_timestamp(seconds)}]\n" | |
| text += f"{output['text']}\n" | |
| text += f"[Translation]\n{get_translation(output['text'])}\n\n" | |
| return text | |
| else: | |
| text = pipe(audio)["text"] | |
| return text | |
| block = gr.Interface( | |
| fn=transcribe, | |
| inputs=[ | |
| gr.Audio(source="microphone", type="filepath", label="Transcribe from Microphone"), | |
| gr.Text(max_lines=1, placeholder="Enter YouTube Link which has a Chinese video", label="Transcribe from YouTube URL"), | |
| gr.Slider(minimum=30, maximum=300, value=30, step=30, label="Number of seconds to transcribe from YouTube URL") | |
| ], | |
| outputs="text", | |
| title="Whisper Small Chinese", | |
| description="Realtime Chinese speech recognition", | |
| ) | |
| block.launch() | |
| ''' | |
| import os | |
| import gradio as gr | |
| from transformers import pipeline | |
| import gradio as gr | |
| import torch | |
| import spacy | |
| os.system('pip install https://huggingface.co/Armandoliv/es_pipeline/resolve/main/es_pipeline-any-py3-none-any.whl') | |
| pipe = pipeline(model="irena/whisper-small-sv-SE") | |
| nlp_ner = spacy.load("es_pipeline") | |
| def main_generator(youtube_id:str): | |
| YouTubeID = youtube_id.split("https://www.youtube.com/watch?v=") # | |
| if len(YouTubeID)>1: | |
| YouTubeID = YouTubeID[1] | |
| else: | |
| YouTubeID ='xOZM-1p-jAk' | |
| OutputFile = f'test_audio_youtube_{YouTubeID}.m4a' | |
| os.system(f"youtube-dl -o {OutputFile} {YouTubeID} --extract-audio --restrict-filenames -f 'bestaudio[ext=m4a]'") | |
| result = pipe(OutputFile) | |
| text = result['text'] | |
| output_list = [] | |
| output_list.append(text) | |
| return text | |
| def transcribe(audio): | |
| text = pipe(audio)["text"] | |
| return text | |
| demo = gr.Blocks() | |
| iface = gr.Interface( | |
| fn=transcribe, | |
| inputs=gr.Audio(source="microphone", type="filepath"), | |
| outputs="text", | |
| title="Whisper Small Swedish-Microphone", | |
| description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model. An audio for recognize.", | |
| ) | |
| inputs = [gr.Textbox(lines=1, placeholder="Link of youtube video here...", label="Input")] | |
| outputs = gr.HighlightedText() | |
| title="Transcription of Swedish videos" | |
| description = "This demo uses small Whisper to transcribe what is spoken in a swedish video" | |
| examples = ['https://www.youtube.com/watch?v=6eWhV7xYH-Q'] | |
| io = gr.Interface(fn=main_generator, inputs=inputs, outputs=outputs, title=title, description = description, examples = examples, | |
| css= """.gr-button-primary { background: -webkit-linear-gradient( | |
| 90deg, #355764 0%, #55a8a1 100% ) !important; background: #355764; | |
| background: linear-gradient( | |
| 90deg, #355764 0%, #55a8a1 100% ) !important; | |
| background: -moz-linear-gradient( 90deg, #355764 0%, #55a8a1 100% ) !important; | |
| background: -webkit-linear-gradient( | |
| 90deg, #355764 0%, #55a8a1 100% ) !important; | |
| color:white !important}""" | |
| ) | |
| with demo: | |
| gr.TabbedInterface([iface, yt], ["Transcribe Audio", "Transcribe YouTube"]) | |
| demo.launch(enable_queue=True) | |
| ''' | |