| # import whisper | |
| import gradio as gr | |
| import datetime | |
| import subprocess | |
| import wave | |
| import contextlib | |
| # import torch | |
| # import pyannote.audio | |
| # from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding | |
| # from pyannote.audio import Audio | |
| # from pyannote.core import Segment | |
| # from sklearn.cluster import AgglomerativeClustering | |
| # import numpy as np | |
| # model = whisper.load_model("large-v2") | |
| # embedding_model = PretrainedSpeakerEmbedding( | |
| # "speechbrain/spkrec-ecapa-voxceleb", | |
| # device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| # ) | |
| # def transcribe(audio, num_speakers): | |
| # path, error = convert_to_wav(audio) | |
| # if error is not None: | |
| # return error | |
| # duration = get_duration(path) | |
| # if duration > 4 * 60 * 60: | |
| # return "Audio duration too long" | |
| # result = model.transcribe(path) | |
| # segments = result["segments"] | |
| # num_speakers = min(max(round(num_speakers), 1), len(segments)) | |
| # if len(segments) == 1: | |
| # segments[0]['speaker'] = 'SPEAKER 1' | |
| # else: | |
| # embeddings = make_embeddings(path, segments, duration) | |
| # add_speaker_labels(segments, embeddings, num_speakers) | |
| # output = get_output(segments) | |
| # return output | |
| # def convert_to_wav(path): | |
| # if path[-3:] != 'wav': | |
| # new_path = '.'.join(path.split('.')[:-1]) + '.wav' | |
| # try: | |
| # subprocess.call(['ffmpeg', '-i', path, new_path, '-y']) | |
| # except: | |
| # return path, 'Error: Could not convert file to .wav' | |
| # path = new_path | |
| # return path, None | |
| # def get_duration(path): | |
| # with contextlib.closing(wave.open(path,'r')) as f: | |
| # frames = f.getnframes() | |
| # rate = f.getframerate() | |
| # return frames / float(rate) | |
| # def make_embeddings(path, segments, duration): | |
| # embeddings = np.zeros(shape=(len(segments), 192)) | |
| # for i, segment in enumerate(segments): | |
| # embeddings[i] = segment_embedding(path, segment, duration) | |
| # return np.nan_to_num(embeddings) | |
| # audio = Audio() | |
| # def segment_embedding(path, segment, duration): | |
| # start = segment["start"] | |
| # # Whisper overshoots the end timestamp in the last segment | |
| # end = min(duration, segment["end"]) | |
| # clip = Segment(start, end) | |
| # waveform, sample_rate = audio.crop(path, clip) | |
| # return embedding_model(waveform[None]) | |
| # def add_speaker_labels(segments, embeddings, num_speakers): | |
| # clustering = AgglomerativeClustering(num_speakers).fit(embeddings) | |
| # labels = clustering.labels_ | |
| # for i in range(len(segments)): | |
| # segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1) | |
| # def time(secs): | |
| # return datetime.timedelta(seconds=round(secs)) | |
| # def get_output(segments): | |
| # output = '' | |
| # for (i, segment) in enumerate(segments): | |
| # if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]: | |
| # if i != 0: | |
| # output += '\n\n' | |
| # output += segment["speaker"] + ' ' + str(time(segment["start"])) + '\n\n' | |
| # output += segment["text"][1:] + ' ' | |
| # return output | |
| s = "" | |
| def greet1(name): | |
| global s | |
| s = "modified" | |
| return "Hello " + name + "!" | |
| def greet2(name): | |
| return "Hi " + name + "!" + " " + s | |
| def greet3(name): | |
| return "Hola " + name + "!" | |
| with gr.Blocks() as demo: | |
| with gr.Box(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio_file = gr.File(label="Upload a Audio file (.wav)", file_count=1) | |
| # name = gr.Textbox(label="Name", placeholder="Name") # TODO: remove | |
| number_of_speakers = gr.Number(label="Number of Speakers", value=2) | |
| with gr.Row(): | |
| btn_clear = gr.Button(value="Clear") | |
| btn_submit = gr.Button(value="Submit") | |
| with gr.Column(): | |
| title = gr.Textbox(label="Title", placeholder="Title for Conversation") | |
| short_summary = gr.Textbox(label="Short Summary", placeholder="Short Summary for Conversation") | |
| sentiment_analysis = gr.Textbox(label="Sentiment Analysis", placeholder="Sentiment Analysis for Conversation") | |
| quality = gr.Textbox(label="Quality of Conversation", placeholder="Quality of Conversation") | |
| detailed_summary = gr.Textbox(label="Detailed Summary", placeholder="Detailed Summary for Conversation") | |
| gr.Markdown("## Examples") | |
| gr.Examples( | |
| examples=[ | |
| [ | |
| "Harsh", | |
| 2, | |
| ], | |
| [ | |
| "Rahul", | |
| 2, | |
| ], | |
| ], | |
| inputs=[title], | |
| outputs=[short_summary], | |
| fn=greet1, | |
| cache_examples=True, | |
| ) | |
| gr.Markdown( | |
| """ | |
| See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft) | |
| for more details. | |
| """ | |
| ) | |
| demo.launch() | |