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app.py

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+#Importing all the necessary packages
+import nltk
+import librosa
+import IPython.display
+import torch
+import gradio as gr
+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)
+
+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)
+
+    # 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
+
+        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]
+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 Audio Transcriptor",
+             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()
+