Update app.py

app.py

@@ -1,14 +1,11 @@
 import torch
-
 import gradio as gr
 import yt_dlp as youtube_dl
 import numpy as np
 from datasets import Dataset, Audio
 from scipy.io import wavfile
-
 from transformers import pipeline
 from transformers.pipelines.audio_utils import ffmpeg_read
-
 import tempfile
 import os
 import time
@@ -29,14 +26,13 @@ pipe = pipeline(
     device=device,
 )
 
-separator = demucs.api.Separator(model = DEMUCS_MODEL_NAME, )
+separator = demucs.api.Separator(model=DEMUCS_MODEL_NAME, )
 
 def separate_vocal(path):
     origin, separated = separator.separate_audio_file(path)
     demucs.api.save_audio(separated["vocals"], path, samplerate=separator.samplerate)
     return path
 
-    
 def transcribe(inputs_path, task, use_demucs, dataset_name, oauth_token: gr.OAuthToken | None, progress=gr.Progress()):
     if inputs_path is None:
         raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
@@ -66,19 +62,15 @@ def transcribe(inputs_path, task, use_demucs, dataset_name, oauth_token: gr.OAut
     current_step += 1
     progress((current_step, total_step), desc="Create dataset.")
     
-
     transcripts = []
     audios = []
     with tempfile.TemporaryDirectory() as tmpdirname:
-        for i,chunk in enumerate(progress.tqdm(chunks, desc="Creating dataset (and clean audio if asked for)")):
-            
-            # TODO: make sure 1D or 2D?
+        for i, chunk in enumerate(progress.tqdm(chunks, desc="Creating dataset (and clean audio if asked for)")):
             arr = chunk["audio"]
             path = os.path.join(tmpdirname, f"{i}.wav")
-            wavfile.write(path, sampling_rate,  arr)
+            wavfile.write(path, sampling_rate, arr)
             
             if use_demucs == "separate-audio":
-                # use demucs tp separate vocals
                 print(f"Separating vocals #{i}")
                 path = separate_vocal(path)
                 
@@ -93,7 +85,6 @@ def transcribe(inputs_path, task, use_demucs, dataset_name, oauth_token: gr.OAut
         
     return [[transcript] for transcript in transcripts], text
 
-
 def _return_yt_html_embed(yt_url):
     video_id = yt_url.split("?v=")[-1]
     HTML_str = (
@@ -133,10 +124,7 @@ def download_yt_audio(yt_url, filename):
         except youtube_dl.utils.ExtractorError as err:
             raise gr.Error(str(err))
 
-
-def yt_transcribe(yt_url, task, use_demucs, dataset_name, oauth_token: gr.OAuthToken | None, max_filesize=75.0, dataset_sampling_rate = 24000,
-                  progress=gr.Progress()):
-    
+def yt_transcribe(yt_url, task, use_demucs, dataset_name, oauth_token: gr.OAuthToken | None, max_filesize=75.0, dataset_sampling_rate=24000, progress=gr.Progress()):
     if yt_url is None:
         raise gr.Error("No youtube link submitted! Please put a working link.")
     if dataset_name is None:
@@ -149,7 +137,7 @@ def yt_transcribe(yt_url, task, use_demucs, dataset_name, oauth_token: gr.OAuthT
 
     if oauth_token is None:
         gr.Warning("Make sure to click and login before using this demo.")
-        return  html_embed_str, [["transcripts will appear here"]], ""
+        return html_embed_str, [["transcripts will appear here"]], ""
         
     current_step += 1
     progress((current_step, total_step), desc="Load video.")
@@ -182,15 +170,12 @@ def yt_transcribe(yt_url, task, use_demucs, dataset_name, oauth_token: gr.OAuthT
     transcripts = []
     audios = []
     with tempfile.TemporaryDirectory() as tmpdirname:
-        for i,chunk in enumerate(progress.tqdm(chunks, desc="Creating dataset (and clean audio if asked for).")):
-            
-            # TODO: make sure 1D or 2D?
+        for i, chunk in enumerate(progress.tqdm(chunks, desc="Creating dataset (and clean audio if asked for).")):
             arr = chunk["audio"]
             path = os.path.join(tmpdirname, f"{i}.wav")
-            wavfile.write(path, dataset_sampling_rate,  arr)
+            wavfile.write(path, dataset_sampling_rate, arr)
             
             if use_demucs == "separate-audio":
-                # use demucs tp separate vocals
                 print(f"Separating vocals #{i}")
                 path = separate_vocal(path)
                 
@@ -203,110 +188,19 @@ def yt_transcribe(yt_url, task, use_demucs, dataset_name, oauth_token: gr.OAuthT
         progress((current_step, total_step), desc="Push dataset.")
         dataset.push_to_hub(dataset_name, token=oauth_token.token if oauth_token else oauth_token)
         
-
     return html_embed_str, [[transcript] for transcript in transcripts], text
 
-
-def naive_postprocess_whisper_chunks(chunks, audio_array, sampling_rate,  stop_chars = ".!:;?", min_duration = 5):
-    # merge chunks as long as merged audio duration is lower than min_duration and that a stop character is not met
-    # return list of dictionnaries (text, audio)
-    # min duration is in seconds
+def naive_postprocess_whisper_chunks(chunks, audio_array, sampling_rate, stop_chars=".!:;?", min_duration=5):
     min_duration = int(min_duration * sampling_rate)
-
-    
     new_chunks = []
     while chunks:
         current_chunk = chunks.pop(0)
-        
         begin, end = current_chunk["timestamp"]
-        begin, end = int(begin*sampling_rate), int(end*sampling_rate)
-        
-        current_dur = end-begin
-        
+        begin, end = int(begin * sampling_rate), int(end * sampling_rate)
+        current_dur = end - begin
         text = current_chunk["text"]
-        
-            
         chunk_to_concat = [audio_array[begin:end]]
-        while chunks and (text[-1] not in stop_chars or (current_dur<min_duration)):
+        while chunks and (text[-1] not in stop_chars or (current_dur < min_duration)):
             ch = chunks.pop(0)
             begin, end = ch["timestamp"]
-            begin, end = int(begin*sampling_rate), int(end*sampling_rate)
-            current_dur += end-begin
-            
-            text = "".join([text, ch["text"]])
-            
-            # TODO: add silence ?
-            chunk_to_concat.append(audio_array[begin:end])
-            
-
-        new_chunks.append({
-            "text": text.strip(),
-            "audio": np.concatenate(chunk_to_concat),
-        })
-        print(f"LENGTH CHUNK #{len(new_chunks)}: {current_dur/sampling_rate}s")
-                
-    return new_chunks
-
-css = """
-#intro{
-    max-width: 100%;
-    text-align: center;
-    margin: 0 auto;
-}
-"""
-with gr.Blocks(css=css) as demo:
-    with gr.Row():
-        gr.LoginButton()
-        gr.LogoutButton()
-        
-    with gr.Tab("YouTube"):
-        gr.Markdown("Create your own TTS dataset using Youtube", elem_id="intro")
-        gr.Markdown(        
-    "This demo allows use to create a text-to-speech dataset from an input audio snippet and push it to hub to keep track of it."
-    f"Demo uses the checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to automatically transcribe audio files"
-    " of arbitrary length. It then merge chunks of audio and push it to the hub."
-    )
-        with gr.Row():
-            with gr.Column():
-                audio_youtube = gr.Textbox(lines=1, placeholder="Paste the URL to a YouTube video here", label="YouTube URL")
-                task_youtube = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
-                cleaning_youtube = gr.Radio(["no-post-processing", "separate-audio"], label="Audio separation and cleaning (takes longer - use it if your samples are not cleaned (background noise and music))", value="separate-audio")
-                textbox_youtube = gr.Textbox(lines=1, placeholder="Place your new dataset name here. Should be in the format : <user>/<dataset_name> or <org>/<dataset_name>. Also accepts <dataset_name>, which will default to the namespace of the logged-in user.", label="Dataset name")
-                
-                with gr.Row():
-                    clear_youtube = gr.ClearButton([audio_youtube, task_youtube, cleaning_youtube, textbox_youtube])
-                    submit_youtube = gr.Button("Submit")
-                
-            with gr.Column():
-                html_youtube = gr.HTML()
-                dataset_youtube = gr.Dataset(label="Transcribed samples.",components=["text"], headers=["Transcripts"], samples=[["transcripts will appear here"]])
-                transcript_youtube = gr.Textbox(label="Transcription")
-        
-    with gr.Tab("Microphone or Audio file"):
-        gr.Markdown("Create your own TTS dataset using your own recordings", elem_id="intro")
-        gr.Markdown(        
-    "This demo allows use to create a text-to-speech dataset from an input audio snippet and push it to hub to keep track of it."
-    f"Demo uses the checkpoint [{MODEL_NAME}](https://huggingface.co/{MODEL_NAME}) and 🤗 Transformers to automatically transcribe audio files"
-    " of arbitrary length. It then merge chunks of audio and push it to the hub."
-    )
-        with gr.Row():
-            with gr.Column():
-                audio_file = gr.Audio(type="filepath")
-                task_file = gr.Radio(["transcribe", "translate"], label="Task", value="transcribe")
-                cleaning_file = gr.Radio(["no-post-processing", "separate-audio"], label="Audio separation and cleaning (takes longer - use it if your samples are not cleaned (background noise and music))", value="separate-audio")
-                textbox_file = gr.Textbox(lines=1, placeholder="Place your new dataset name here. Should be in the format : <user>/<dataset_name> or <org>/<dataset_name>. Also accepts <dataset_name>, which will default to the namespace of the logged-in user.", label="Dataset name")
-                
-                with gr.Row():
-                    clear_file = gr.ClearButton([audio_file, task_file, cleaning_file, textbox_file])
-                    submit_file = gr.Button("Submit")
-                
-            with gr.Column():
-                dataset_file = gr.Dataset(label="Transcribed samples.", components=["text"], headers=["Transcripts"], samples=[["transcripts will appear here"]])
-                transcript_file = gr.Textbox(label="Transcription")
-                
-                
-    
-    submit_file.click(transcribe, inputs=[audio_file, task_file, cleaning_file, textbox_file], outputs=[dataset_file, transcript_file])                
-    submit_youtube.click(yt_transcribe, inputs=[audio_youtube, task_youtube, cleaning_youtube, textbox_youtube], outputs=[html_youtube, dataset_youtube, transcript_youtube])                
-    
-demo.launch(debug=True)
+            begin, end = int(begin * sampling_rate), int(end * sampling_rate)