Update app.py

app.py

@@ -1,84 +1,89 @@
 #Importing all the necessary packages
-import nltk
-import librosa
-import IPython.display
-import torch
+# import nltk
+# import librosa
+# import IPython.display
+# import torch
 import gradio as gr
-from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
-nltk.download("punkt")
+# from transformers import Wav2Vec2Tokenizer, Wav2Vec2ForCTC
+# nltk.download("punkt")
 #Loading the model and the tokenizer
-model_name = "facebook/wav2vec2-base-960h"
-tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)#omdel_name
-model = Wav2Vec2ForCTC.from_pretrained(model_name)
+# model_name = "facebook/wav2vec2-base-960h"
+# tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name)#omdel_name
+# model = Wav2Vec2ForCTC.from_pretrained(model_name)
 
-def load_data(input_file):
-    """ Function for resampling to ensure that the speech input is sampled at 16KHz.
-    """
-    #read the file
-    speech, sample_rate = librosa.load(input_file)
-    #make it 1-D
-    if len(speech.shape) > 1: 
-        speech = speech[:,0] + speech[:,1]
-    #Resampling at 16KHz since wav2vec2-base-960h is pretrained and fine-tuned on speech audio sampled at 16 KHz.
-    if sample_rate !=16000:
-        speech = librosa.resample(speech, sample_rate,16000)
-    #speeches = librosa.effects.split(speech)
-    return speech
-def correct_casing(input_sentence):
-    """ This function is for correcting the casing of the generated transcribed text
-    """
-    sentences = nltk.sent_tokenize(input_sentence)
-    return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences]))
+# def load_data(input_file):
+#     """ Function for resampling to ensure that the speech input is sampled at 16KHz.
+#     """
+#     #read the file
+#     speech, sample_rate = librosa.load(input_file)
+#     #make it 1-D
+#     if len(speech.shape) > 1: 
+#         speech = speech[:,0] + speech[:,1]
+#     #Resampling at 16KHz since wav2vec2-base-960h is pretrained and fine-tuned on speech audio sampled at 16 KHz.
+#     if sample_rate !=16000:
+#         speech = librosa.resample(speech, sample_rate,16000)
+#     #speeches = librosa.effects.split(speech)
+#     return speech
+# def correct_casing(input_sentence):
+#     """ This function is for correcting the casing of the generated transcribed text
+#     """
+#     sentences = nltk.sent_tokenize(input_sentence)
+#     return (' '.join([s.replace(s[0],s[0].capitalize(),1) for s in sentences]))
 
-def asr_transcript(input_file):
-    """This function generates transcripts for the provided audio input
-    """
-    speech = load_data(input_file)
-    #Tokenize
-    input_values = tokenizer(speech, return_tensors="pt").input_values
-    #Take logits
-    logits = model(input_values).logits
-    #Take argmax
-    predicted_ids = torch.argmax(logits, dim=-1)
-    #Get the words from predicted word ids
-    transcription = tokenizer.decode(predicted_ids[0])
-    #Output is all upper case
-    transcription = correct_casing(transcription.lower())
-    return transcription
-def asr_transcript_long(input_file,tokenizer=tokenizer, model=model ):
-    transcript = ""
-    # Ensure that the sample rate is 16k
-    sample_rate = librosa.get_samplerate(input_file)
+# def asr_transcript(input_file):
+#     """This function generates transcripts for the provided audio input
+#     """
+#     speech = load_data(input_file)
+#     #Tokenize
+#     input_values = tokenizer(speech, return_tensors="pt").input_values
+#     #Take logits
+#     logits = model(input_values).logits
+#     #Take argmax
+#     predicted_ids = torch.argmax(logits, dim=-1)
+#     #Get the words from predicted word ids
+#     transcription = tokenizer.decode(predicted_ids[0])
+#     #Output is all upper case
+#     transcription = correct_casing(transcription.lower())
+#     return transcription
+# def asr_transcript_long(input_file,tokenizer=tokenizer, model=model ):
+#     transcript = ""
+#     # Ensure that the sample rate is 16k
+#     sample_rate = librosa.get_samplerate(input_file)
 
-    # Stream over 10 seconds chunks rather than load the full file
-    stream = librosa.stream(
-        input_file,
-        block_length=20, #number of seconds to split the batch
-        frame_length=sample_rate, #16000,
-        hop_length=sample_rate, #16000
-    )
+#     # Stream over 10 seconds chunks rather than load the full file
+#     stream = librosa.stream(
+#         input_file,
+#         block_length=20, #number of seconds to split the batch
+#         frame_length=sample_rate, #16000,
+#         hop_length=sample_rate, #16000
+#     )
 
-    for speech in stream:
-        if len(speech.shape) > 1:
-            speech = speech[:, 0] + speech[:, 1]
-        if sample_rate !=16000:
-            speech = librosa.resample(speech, sample_rate,16000)
-        input_values = tokenizer(speech, return_tensors="pt").input_values
-        logits = model(input_values).logits
+#     for speech in stream:
+#         if len(speech.shape) > 1:
+#             speech = speech[:, 0] + speech[:, 1]
+#         if sample_rate !=16000:
+#             speech = librosa.resample(speech, sample_rate,16000)
+#         input_values = tokenizer(speech, return_tensors="pt").input_values
+#         logits = model(input_values).logits
 
-        predicted_ids = torch.argmax(logits, dim=-1)
-        transcription = tokenizer.decode(predicted_ids[0])
-        #transcript += transcription.lower()
-        transcript += correct_casing(transcription.lower())
-        #transcript += " "
+#         predicted_ids = torch.argmax(logits, dim=-1)
+#         transcription = tokenizer.decode(predicted_ids[0])
+#         #transcript += transcription.lower()
+#         transcript += correct_casing(transcription.lower())
+#         #transcript += " "
+
+#     return transcript[:3800]
+from transformers import pipeline
+p=pipeline("automatic-speech-recognition", model="facebook/wav2vec2-base-960h")
+
+def asr_transcript_long(input_file):
+    return p(input_file, chunk_length_s=10, stride_length_s=(2, 2))['text']
 
-    return transcript[:3800]
 gr.Interface(asr_transcript_long,
              #inputs = gr.inputs.Audio(source="microphone", type="filepath", optional=True, label="Please record your voice"),
              inputs = gr.inputs.Audio(source="upload", type="filepath", optional=True, label="Upload your audio file here"),
              outputs = gr.outputs.Textbox(type="str",label="Output Text"),
              title="English Automated Speech Summarization",
              description = "This tool transcribes your audio to the text",
-             # examples = [["Batman1_dialogue.wav"], ["Batman2_dialogue.wav"], ["Batman3_dialogue.wav"],["catwoman_dialogue.wav"]], 
-             theme="grass").launch()
-
+             examples = [["sample 1.flac"], ["sample 2.flac"], ["sample 3.flac"],["TheDiverAnUncannyTale.mp3"]], 
+             theme="grass").launch()