a

.history/app_20230718133117.py

@@ -1,24 +0,0 @@
-import gradio as gr
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-import torch
-import phonemizer
-import librosa
-
-processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-
-waveform, sample_rate = librosa.load('harvard.wav', sr=16000) # Downsample 44.1kHz to 8kHz
-
-input_values = processor(waveform, sampling_rate=sample_rate, return_tensors="pt").input_values
-
-with torch.no_grad():
-   logits = model(input_values).logits
-
-predicted_ids = torch.argmax(logits, dim=-1)
-transcription = processor.batch_decode(predicted_ids)
-
-def showTranscription(transcription):
-   return transcription
-
-iface = gr.Interface(fn=showTranscription, inputs="text", outputs="text")
-iface.launch()

.history/app_20230718133128.py

@@ -1,24 +0,0 @@
-import gradio as gr
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-import torch
-import phonemizer
-import librosa
-
-processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-
-waveform, sample_rate = librosa.load('harvard.wav', sr=16000) # Downsample 44.1kHz to 8kHz
-
-input_values = processor(waveform, sampling_rate=sample_rate, return_tensors="pt").input_values
-
-with torch.no_grad():
-   logits = model(input_values).logits
-
-predicted_ids = torch.argmax(logits, dim=-1)
-transcription = processor.batch_decode(predicted_ids)
-
-def showTranscription(transcription):
-   return transcription
-
-iface = gr.Interface(fn=showTranscription, inputs="text", outputs="text")
-iface.launch()

.history/app_20230718133340.py

@@ -1,32 +0,0 @@
-import gradio as gr
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-import torch
-import phonemizer
-import librosa
-import base64
-
-
-processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-
-waveform, sample_rate = librosa.load(
-    "harvard.wav", sr=16000
-)  # Downsample 44.1kHz to 8kHz
-
-input_values = processor(
-    waveform, sampling_rate=sample_rate, return_tensors="pt"
-).input_values
-
-with torch.no_grad():
-    logits = model(input_values).logits
-
-predicted_ids = torch.argmax(logits, dim=-1)
-transcription = processor.batch_decode(predicted_ids)
-
-
-def showTranscription(transcription):
-    return transcription
-
-
-iface = gr.Interface(fn=showTranscription, inputs="text", outputs="text")
-iface.launch()

.history/app_20230718133558.py

@@ -1,33 +0,0 @@
-import gradio as gr
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-import torch
-import phonemizer
-import librosa
-import base64
-
-
-def lark(audioAsB64):
-    with open("audio.wav", "wb") as preWaveform:
-        preWaveform.write(base64.b64encode())
-
-
-processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-
-waveform, sample_rate = librosa.load(
-    "harvard.wav", sr=16000
-)  # Downsample 44.1kHz to 8kHz
-
-input_values = processor(
-    waveform, sampling_rate=sample_rate, return_tensors="pt"
-).input_values
-
-with torch.no_grad():
-    logits = model(input_values).logits
-
-predicted_ids = torch.argmax(logits, dim=-1)
-transcription = processor.batch_decode(predicted_ids)
-
-
-iface = gr.Interface(fn=lark, inputs="text", outputs="text")
-iface.launch()

.history/app_20230718133701.py

@@ -1,36 +0,0 @@
-import gradio as gr
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-import torch
-import phonemizer
-import librosa
-import base64
-
-
-def lark(audioAsB64):
-    # convert b64 audio to wav
-    with open("audio.wav", "wb") as preWaveform:
-        preWaveform.write(base64.b64encode())
-
-    # processing
-    processor = Wav2Vec2Processor.from_pretrained(
-        "facebook/wav2vec2-xlsr-53-espeak-cv-ft"
-    )
-    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-
-    waveform, sample_rate = librosa.load(
-        "harvard.wav", sr=16000
-    )  # Downsample 44.1kHz to 8kHz
-
-    input_values = processor(
-        waveform, sampling_rate=sample_rate, return_tensors="pt"
-    ).input_values
-
-    with torch.no_grad():
-        logits = model(input_values).logits
-
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = processor.batch_decode(predicted_ids)
-
-
-iface = gr.Interface(fn=lark, inputs="text", outputs="text")
-iface.launch()

.history/app_20230718133728.py

@@ -1,38 +0,0 @@
-import gradio as gr
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-import torch
-import phonemizer
-import librosa
-import base64
-
-
-def lark(audioAsB64):
-    # convert b64 audio to wav
-    with open("audio.wav", "wb") as preWaveform:
-        preWaveform.write(base64.b64encode())
-
-    # processing
-    processor = Wav2Vec2Processor.from_pretrained(
-        "facebook/wav2vec2-xlsr-53-espeak-cv-ft"
-    )
-    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-
-    waveform, sample_rate = librosa.load(
-        "harvard.wav", sr=16000
-    )  # Downsample 44.1kHz to 8kHz
-
-    input_values = processor(
-        waveform, sampling_rate=sample_rate, return_tensors="pt"
-    ).input_values
-
-    with torch.no_grad():
-        logits = model(input_values).logits
-
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = processor.batch_decode(predicted_ids)
-
-    return transcription
-
-
-iface = gr.Interface(fn=lark, inputs="text", outputs="text")
-iface.launch()

.history/app_20230718134339.py

@@ -1,38 +0,0 @@
-import gradio as gr
-from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
-import torch
-import phonemizer
-import librosa
-import base64
-
-
-def lark(audioAsB64):
-    # convert b64 audio to wav
-    with open("audio.wav", "wb") as preWaveform:
-        preWaveform.write(base64.b64encode())
-
-    # processing
-    processor = Wav2Vec2Processor.from_pretrained(
-        "facebook/wav2vec2-xlsr-53-espeak-cv-ft"
-    )
-    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft")
-
-    waveform, sample_rate = librosa.load(
-        "harvard.wav", sr=16000
-    )  # Downsample 44.1kHz to 8kHz
-
-    input_values = processor(
-        waveform, sampling_rate=sample_rate, return_tensors="pt"
-    ).input_values
-
-    with torch.no_grad():
-        logits = model(input_values).logits
-
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = processor.batch_decode(predicted_ids)
-
-    return transcription
-
-
-iface = gr.Interface(fn=lark, inputs="text", outputs="text")
-iface.launch()

.history/packages_20230718132842.txt

@@ -1 +0,0 @@
-espeak

.history/requirements_20230718132835.txt

@@ -1,4 +0,0 @@
-phonemizer
-librosa
-transformers
-torch

.history/requirements_20230718133331.txt

@@ -1,5 +0,0 @@
-phonemizer
-librosa
-transformers
-torch
-base64

.history/requirements_20230718134813.txt

@@ -1,4 +0,0 @@
-phonemizer
-librosa
-transformers
-torch

.history/requirements_20230718134828.txt

@@ -1,4 +0,0 @@
-phonemizer
-librosa
-transformers
-torch