Updated pataka working with syllables

app.py

@@ -4,6 +4,7 @@ import torch
 import numpy as np
 import soundfile as sf
 import io
+import librosa
 
 st.title("Syllables per Second Calculator")
 st.write("Upload an audio file to calculate the number of 'p', 't', and 'k' syllables per second.")
@@ -12,8 +13,17 @@ def get_syllables_per_second(audio_file):
     processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
     model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
 
-    audio_input, sample_rate = sf.read(io.BytesIO(audio_file.read()))
+    audio_input, original_sample_rate = sf.read(io.BytesIO(audio_file.read()))
+    target_sample_rate = processor.feature_extractor.sampling_rate
 
+    # resample the sample rate if not 16 k
+    if original_sample_rate != target_sample_rate:
+        if audio_input.ndim > 1:
+            audio_input = np.asarray([librosa.resample(channel, orig_sr=original_sample_rate, target_sr=target_sample_rate) for channel in audio_input.T]).T
+        else:
+            audio_input = librosa.resample(audio_input, orig_sr=original_sample_rate, target_sr=target_sample_rate)
+
+    # make the audio mono if it is stereo
     if audio_input.ndim > 1 and audio_input.shape[1] == 2:
         audio_input = np.mean(audio_input, axis=1)
 
@@ -24,19 +34,29 @@ def get_syllables_per_second(audio_file):
         predicted_ids = torch.argmax(logits, dim=-1)
         transcription = processor.batch_decode(predicted_ids, output_char_offsets=True)
         offsets = transcription['char_offsets']
+        print("the offets are: ", offsets)
 
     # Find the start and end time offsets of the syllables
+
     syllable_offsets = [item for item in offsets[0] if item['char'] in ['p', 't', 'k']]
     
     if syllable_offsets:  # if any syllable is found
-        first_syllable_offset = syllable_offsets[0]['start_offset'] / sample_rate
-        last_syllable_offset = syllable_offsets[-1]['end_offset'] / sample_rate
+        first_syllable_offset = syllable_offsets[0]['start_offset'] * 0.02
+        last_syllable_offset = syllable_offsets[-1]['end_offset'] * 0.02
+
+        print("the first syllable offset is: ", first_syllable_offset)
+        print("the last syllable offset is: ", last_syllable_offset)
         # Duration from the first to the last syllable
         syllable_duration = last_syllable_offset - first_syllable_offset
+        print("the syllable duration is: ", syllable_duration)
     else:
         syllable_duration = 0
 
     syllable_count = len(syllable_offsets)
+    audio_duration = len(audio_input) / target_sample_rate
+    print("the audio duration is: ", audio_duration)
+    print("the syllable count is: ", syllable_count)
+    #print("the syllabels per second is: ", syllable_count / audio_duration)
     syllables_per_second = syllable_count / syllable_duration if syllable_duration > 0 else 0
 
     return syllables_per_second
@@ -46,4 +66,4 @@ uploaded_file = st.file_uploader("Choose an audio file", type=["wav"])
 if uploaded_file is not None:
     with st.spinner("Processing the audio file..."):
         result = get_syllables_per_second(uploaded_file)
-        st.write("Syllables per second: ", result)
+        st.write("Syllables per second: ", result)

requirements.txt

@@ -2,4 +2,5 @@ torch
 numpy
 transformers
 soundfile
-phonemizer
+phonemizer
+librosa