Update app.py

app.py

@@ -1,27 +1,20 @@
-import os
 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
-import gradio as gr
-
-from huggingface_hub import login, HfApi
-# Ensure you have logged in to Hugging Face Hub
-HF_API_TOKEN = os.getenv('OAUTH_CLIENT_SECRET')
+import demucs.api
 
-css = """
-#intro{
-    max-width: 100%;
-    text-align: center;
-    margin: 0 auto;
-}
-"""
-
-MODEL_NAME = "openai/whisper-large-v3"  
+MODEL_NAME = "openai/whisper-large-v3" # "patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram" # 
 DEMUCS_MODEL_NAME = "htdemucs_ft"
 BATCH_SIZE = 8
 FILE_LIMIT_MB = 1000
@@ -36,20 +29,78 @@ 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.")
+    if dataset_name is None:
+        raise gr.Error("No dataset name submitted! Please submit a dataset name. 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.")
+
+    if oauth_token is None:
+        gr.Warning("Make sure to click and login before using this demo.")
+        return [["transcripts will appear here"]], ""
+    
+    total_step = 4
+    current_step = 0
+    
+    current_step += 1
+    progress((current_step, total_step), desc="Transcribe using Whisper.")
+
+    sampling_rate, inputs = wavfile.read(inputs_path) 
+
+    out = pipe(inputs_path, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
+    
+    text = out["text"]
+    
+    current_step += 1
+    progress((current_step, total_step), desc="Merge chunks.")
+    chunks = naive_postprocess_whisper_chunks(out["chunks"], inputs, sampling_rate)
+
+    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?
+            arr = chunk["audio"]
+            path = os.path.join(tmpdirname, f"{i}.wav")
+            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)
+                
+            audios.append(path)
+            transcripts.append(chunk["text"])
+            
+        dataset = Dataset.from_dict({"audio": audios, "text": transcripts}).cast_column("audio", Audio())
+        
+        current_step += 1
+        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 [[transcript] for transcript in transcripts], 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 gr.HTML(value=HTML_str)
+    return HTML_str
 
 def download_yt_audio(yt_url, filename):
     info_loader = youtube_dl.YoutubeDL()
@@ -70,8 +121,8 @@ def download_yt_audio(yt_url, filename):
     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("%H:%M:%S", time.gmtime(YT_LENGTH_LIMIT_S))
-        file_length_hms = time.strftime("%H:%M:%S", time.gmtime(file_length_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"}
@@ -82,31 +133,48 @@ def download_yt_audio(yt_url, filename):
         except youtube_dl.utils.ExtractorError as err:
             raise gr.Error(str(err))
 
-def transcribe(inputs_path, task, use_demucs, dataset_name, oauth_token, 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.")
-    if dataset_name is None:
-        raise gr.Error("No dataset name submitted! Please submit a dataset name. 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.")
 
-    if oauth_token is None:
-        gr.Warning("Make sure to click and login before using this demo.")
-        return ["transcripts will appear here"]
+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()):
     
-    total_step = 4
+    if yt_url is None:
+        raise gr.Error("No youtube link submitted! Please put a working link.")
+    if dataset_name is None:
+        raise gr.Error("No dataset name submitted! Please submit a dataset name. 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.")
+
+    total_step = 5
     current_step = 0
     
+    html_embed_str = _return_yt_html_embed(yt_url)
+
+    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"]], ""
+        
     current_step += 1
-    progress((current_step, total_step), desc="Transcribe using Whisper.")
+    progress((current_step, total_step), desc="Load video.")
 
-    sampling_rate, inputs = wavfile.read(inputs_path) 
+    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_path = f.read()
 
-    out = pipe(inputs_path, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
+        inputs = ffmpeg_read(inputs_path, pipe.feature_extractor.sampling_rate)
+        inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
     
-    text = out["text"]
+        current_step += 1
+        progress((current_step, total_step), desc="Transcribe using Whisper.")
+        out = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": task}, return_timestamps=True)
+        
+        text = out["text"]
+            
+        inputs = ffmpeg_read(inputs_path, dataset_sampling_rate)
     
     current_step += 1
     progress((current_step, total_step), desc="Merge chunks.")
-    chunks = naive_postprocess_whisper_chunks(out["chunks"], inputs, sampling_rate)
+    chunks = naive_postprocess_whisper_chunks(out["chunks"], inputs, dataset_sampling_rate)
 
     current_step += 1
     progress((current_step, total_step), desc="Create dataset.")
@@ -114,12 +182,16 @@ def transcribe(inputs_path, task, use_demucs, dataset_name, oauth_token, progres
     transcripts = []
     audios = []
     with tempfile.TemporaryDirectory() as tmpdirname:
-        for i, chunk in enumerate(progress.tqdm(chunks, desc="Creating dataset (and clean audio if asked for)")):
+        for i,chunk in enumerate(progress.tqdm(chunks, desc="Creating dataset (and clean audio if asked for).")):
+            
+            # TODO: make sure 1D or 2D?
             arr = chunk["audio"]
             path = os.path.join(tmpdirname, f"{i}.wav")
-            wavfile.write(path, 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)
                 
             audios.append(path)
@@ -131,26 +203,75 @@ def transcribe(inputs_path, task, use_demucs, dataset_name, oauth_token, progres
         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 [[transcript] for transcript in transcripts], text
 
+    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
+    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
+        
+        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)):
+            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 you to create a text-to-speech dataset from an input audio snippet and push it to the 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 merges chunks of audio and pushes them to the hub."
-        )
+        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 clean (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")
+                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])
@@ -158,22 +279,22 @@ with gr.Blocks(css=css) as demo:
                 
             with gr.Column():
                 html_youtube = gr.HTML()
-                dataset_youtube = gr.Dataset(label="Transcribed samples.", components=["text"], headers=["Transcripts"], samples=[["transcripts will appear here"]])
+                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 you to create a text-to-speech dataset from an input audio snippet and push it to the 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 merges chunks of audio and pushes them to the hub."
-        )
+        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 clean (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")
+                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])
@@ -183,7 +304,9 @@ with gr.Blocks(css=css) as demo:
                 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(transcribe, inputs=[audio_youtube, task_youtube, cleaning_youtube, textbox_youtube], outputs=[dataset_youtube, transcript_youtube])                
+    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)
+demo.launch(debug=True)