Update app.py

app.py

@@ -1,20 +1,16 @@
-import torch
-
 import gradio as gr
+import torch
+from transformers import pipeline
 import yt_dlp as youtube_dl
+import os
+from scipy.io import wavfile
 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 demucs.api
+import demucs
 
-MODEL_NAME = "openai/whisper-large-v3" # "patrickvonplaten/wav2vec2-large-960h-lv60-self-4-gram" # 
+MODEL_NAME = "openai/whisper-large-v2"
 DEMUCS_MODEL_NAME = "htdemucs_ft"
 BATCH_SIZE = 8
 FILE_LIMIT_MB = 1000
@@ -29,34 +25,29 @@ pipe = pipeline(
     device=device,
 )
 
-separator = demucs.api.Separator(model = DEMUCS_MODEL_NAME, )
+separator = demucs.pretrained.hdemucs()
 
 def separate_vocal(path):
-    origin, separated = separator.separate_audio_file(path)
-    demucs.api.save_audio(separated["vocals"], path, samplerate=separator.samplerate)
-    return path
+    origin, separated = separator(path)
+    vocal_path = os.path.splitext(path)[0] + "_vocals.wav"
+    wavfile.write(vocal_path, separator.samplerate, separated[1].numpy())
+    return vocal_path
 
-    
-def transcribe(inputs_path, task, use_demucs, dataset_name, oauth_token: gr.OAuthToken | None, progress=gr.Progress()):
+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:
+    if not dataset_name:
         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"]], ""
-    
+        raise gr.Error("No OAuth token submitted! Please login to use this demo.")
+
     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) 
-
+    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
@@ -65,101 +56,49 @@ 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)
                 
             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)
+        dataset.push_to_hub(dataset_name, token=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 HTML_str
-
-def download_yt_audio(yt_url, filename):
-    info_loader = youtube_dl.YoutubeDL()
-    
-    try:
-        info = info_loader.extract_info(yt_url, download=False)
-    except youtube_dl.utils.DownloadError as err:
-        raise gr.Error(str(err))
-    
-    file_length = info["duration_string"]
-    file_h_m_s = file_length.split(":")
-    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
-    
-    if len(file_h_m_s) == 1:
-        file_h_m_s.insert(0, 0)
-    if len(file_h_m_s) == 2:
-        file_h_m_s.insert(0, 0)
-    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("%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"}
-    
-    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
-        try:
-            ydl.download([yt_url])
-        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, 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:
+        raise gr.Error("No YouTube URL submitted! Please provide a working link.")
+    if not dataset_name:
         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:
+        raise gr.Error("No OAuth token submitted! Please login to use this demo.")
 
     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="Load video.")
 
     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()
+        inputs_path = filepath
 
         inputs = ffmpeg_read(inputs_path, pipe.feature_extractor.sampling_rate)
         inputs = {"array": inputs, "sampling_rate": pipe.feature_extractor.sampling_rate}
@@ -167,7 +106,6 @@ def yt_transcribe(yt_url, task, use_demucs, dataset_name, oauth_token: gr.OAuthT
         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)
@@ -178,135 +116,140 @@ def yt_transcribe(yt_url, task, use_demucs, dataset_name, oauth_token: gr.OAuthT
 
     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, 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)
                 
             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)
+        dataset.push_to_hub(dataset_name, token=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
-            
+            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),
+            "text": text,
+            "audio": np.concatenate(chunk_to_concat)
         })
-        print(f"LENGTH CHUNK #{len(new_chunks)}: {current_dur/sampling_rate}s")
-                
     return new_chunks
 
+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_yt_audio(yt_url, filename):
+    info_loader = youtube_dl.YoutubeDL()
+    try:
+        info = info_loader.extract_info(yt_url, download=False)
+    except youtube_dl.utils.DownloadError as err:
+        raise gr.Error(str(err))
+    
+    file_length = info["duration_string"]
+    file_h_m_s = file_length.split(":")
+    file_h_m_s = [int(sub_length) for sub_length in file_h_m_s]
+    
+    if len(file_h_m_s) == 1:
+        file_h_m_s.insert(0, 0)
+    if len(file_h_m_s) == 2:
+        file_h_m_s.insert(0, 0)
+    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("%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"}
+    
+    with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+        try:
+            ydl.download([yt_url])
+        except youtube_dl.utils.ExtractorError as err:
+            raise gr.Error(str(err))
+
 css = """
-#intro{
-    max-width: 100%;
-    text-align: center;
-    margin: 0 auto;
+#intro {
+  padding: 20px;
+  background-color: #f0f0f0;
+  margin-bottom: 10px;
+}
+#intro h1 {
+  font-size: 30px;
 }
 """
-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."
-    )
+gr.config.css(css)
+
+with gr.Blocks() as demo:
+    with gr.Tab("Local file"):
         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")
-                
+                local_audio_input = gr.Audio(type="filepath", label="Upload Audio")
+                task_input = gr.Dropdown(choices=["transcribe", "translate"], value="transcribe", label="Task")
+                use_demucs_input = gr.Dropdown(choices=["do-nothing", "separate-audio"], value="do-nothing", label="Audio preprocessing")
+                dataset_name_input = gr.Textbox(label="Dataset name")
+                hf_token = gr.Textbox(label="HuggingFace Token")
+                submit_local_button = gr.Button("Transcribe")
             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."
-    )
+                local_output_text = gr.Dataframe(label="Transcripts")
+                local_output_full_text = gr.Textbox(label="Full Text")
+    
+        submit_local_button.click(
+            transcribe,
+            inputs=[local_audio_input, task_input, use_demucs_input, dataset_name_input, hf_token],
+            outputs=[local_output_text, local_output_full_text],
+        )
+
+    with gr.Tab("YouTube video"):
         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")
-                
+                yt_url_input = gr.Textbox(label="YouTube URL")
+                yt_task_input = gr.Dropdown(choices=["transcribe", "translate"], value="transcribe", label="Task")
+                yt_use_demucs_input = gr.Dropdown(choices=["do-nothing", "separate-audio"], value="do-nothing", label="Audio preprocessing")
+                yt_dataset_name_input = gr.Textbox(label="Dataset name")
+                yt_hf_token = gr.Textbox(label="HuggingFace Token")
+                submit_yt_button = gr.Button("Transcribe")
             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")
-                
-                
+                yt_html_embed_str = gr.HTML()
+                yt_output_text = gr.Dataframe(label="Transcripts")
+                yt_output_full_text = gr.Textbox(label="Full Text")
     
-    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)
+        submit_yt_button.click(
+            yt_transcribe,
+            inputs=[yt_url_input, yt_task_input, yt_use_demucs_input, yt_dataset_name_input, yt_hf_token],
+            outputs=[yt_html_embed_str, yt_output_text, yt_output_full_text],
+        )
+
+demo.launch(share=True)