Duplicate from balaramas/indic_s2t

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: Indic S2t
+emoji: 🌖
+colorFrom: blue
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.36.1
+app_file: app.py
+pinned: false
+license: other
+duplicated_from: balaramas/indic_s2t
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+"""
+Script to translate given single english audio file to corresponding hindi text
+Usage : python s2t_en2hi.py <audio_file_path> <averaged_checkpoints_file_path>
+"""
+
+
+
+import gradio as gr
+import sys
+import os
+import subprocess
+from pydub import AudioSegment
+from huggingface_hub import snapshot_download
+
+def install_fairseq():
+    try:
+        # Run pip install command to install fairseq
+        subprocess.check_call(["pip", "install", "fairseq"])
+        subprocess.check_call(["pip", "install", "sentencepiece"])
+        subprocess.check_call(["pip", "install", "soundfile"])
+        return "fairseq successfully installed!"
+    except subprocess.CalledProcessError as e:
+        return f"An error occurred while installing fairseq: {str(e)}"
+
+def convert_audio_to_16k_wav(audio_input):
+    sound = AudioSegment.from_file(audio_input)
+    sample_rate = sound.frame_rate
+    num_channels = sound.channels
+    num_frames = int(sound.frame_count())
+    filename = audio_input.split("/")[-1]
+    print("original file is at:", audio_input)
+    if (num_channels > 1) or (sample_rate != 16000): # convert to mono-channel 16k wav
+        if num_channels > 1:
+            sound = sound.set_channels(1)
+        if sample_rate != 16000:
+            sound = sound.set_frame_rate(16000)
+        num_frames = int(sound.frame_count())
+        filename = filename.replace(".wav", "") + "_16k.wav"
+        sound.export(f"{filename}", format="wav")
+    return filename
+
+
+def run_my_code(input_text, language):
+    # TODO better argument handling
+    audio=convert_audio_to_16k_wav(input_text)
+    hi_wav = audio
+
+    data_root=""
+    model_checkpoint=""
+    d_r=""
+
+    if(language=="Hindi"):
+        model_checkpoint = "./models/hi_m.pt"
+        data_root="./lang/hi/"
+
+    if(language=="Gujrati"):
+        model_checkpoint = "./models/gj_m.pt"
+        data_root="./lang/gj/"
+
+    if(language=="Bengali"):
+        model_checkpoint = "./models/bn_m.pt"
+        data_root="./lang/bn/"        
+
+    if(language=="Nepali"):
+        model_checkpoint = "./models/ne_m.pt"
+        data_root="./lang/ne/"
+
+    if(language=="Tamil"):
+        model_checkpoint = "./models/tm_m.pt"
+        data_root="./lang/tm/"
+
+    if(language=="Marathi"):
+        model_checkpoint = "./models/mt_m.pt"
+        data_root="./lang/mt/"        
+        
+    #os.system(f"cp {hi_wav} {data_root}data/tst-COMMON/wav/test.wav")
+    f = open('input.txt', 'w')
+    f.write(hi_wav)
+
+    f = open('input.txt', 'r')
+    content = f. read()
+    print(content)
+    print(hi_wav)
+
+    print("------Performing translation...")
+    #subprocess.run(["fairseq-interactive", data_root, "--config-yaml", "config_st.yaml", "--task", "speech_to_text", "--path", model_checkpoint, "--max-tokens", "50000", "--beam", "5" ,"--input" ,"input.txt"])
+    translation_result = subprocess.run(["fairseq-interactive", data_root, "--config-yaml", "config_st.yaml", "--task", "speech_to_text", "--path", model_checkpoint, "--max-tokens", "50000", "--beam", "5" ,"--input" ,"input.txt"], capture_output=True, text=True)
+    translation_result_text = translation_result.stdout
+ 
+    lines = translation_result_text.split("\n")
+
+    output_text=""
+    print("\n\n------Translation results are:")
+    for i in lines:
+        if (i.startswith("D-0")):
+            print(i.split("\t")[2])
+            output_text=i.split("\t")[2]
+            break
+
+    #os.system(f"rm {data_root}data/tst-COMMON/wav/test.wav")
+    f = open('input.txt', 'w')
+    f.write("")
+
+    f = open('input.txt', 'r')
+    content = f. read()
+    print(content)
+    return output_text
+
+install_fairseq()
+
+# Define the input and output interfaces for Gradio
+#inputs = [
+  #      gr.inputs.Audio(source="microphone", type="filepath", label="Record something (in English)..."),
+  #      gr.inputs.Dropdown(list(LANGUAGE_CODES.keys()), default="Hindi", label="From English to Languages X..."),
+   # ]
+
+#input_textbox = gr.inputs.Textbox(label="test2.wav")
+#input=gr.inputs.Audio(source="microphone", type="filepath", label="Record something (in English)...")
+#audio=convert_audio_to_16k_wav(input)
+output_textbox = gr.outputs.Textbox(label="Translated Text")
+
+# Create a Gradio interface
+iface = gr.Interface(
+        fn=run_my_code, 
+        inputs=[gr.inputs.Audio(source="microphone", type="filepath", label="Record something (in American English accent)"), gr.inputs.Radio(["Hindi", "Gujrati", "Bengali", "Tamil", "Nepali", "Marathi"], label="Language")], 
+        outputs=output_textbox,
+        title="English to Indic Language Translator")
+
+# Launch the interface
+iface.launch()

lang/bn/config_st.yaml

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+bpe_tokenizer:
+  bpe: sentencepiece
+  sentencepiece_model: ./spm_unigram8000_st.model
+input_channels: 1
+input_feat_per_channel: 80
+specaugment:
+  freq_mask_F: 27
+  freq_mask_N: 1
+  time_mask_N: 1
+  time_mask_T: 100
+  time_mask_p: 1.0
+  time_wrap_W: 0
+transforms:
+  '*':
+  - utterance_cmvn
+  _train:
+  - utterance_cmvn
+  - specaugment
+vocab_filename: spm_unigram8000_st.txt

lang/bn/spm_unigram8000_st.model

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lang/gj/config_st.yaml

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+bpe_tokenizer:
+  bpe: sentencepiece
+  sentencepiece_model: ./spm_unigram8000_st.model
+input_channels: 1
+input_feat_per_channel: 80
+specaugment:
+  freq_mask_F: 27
+  freq_mask_N: 1
+  time_mask_N: 1
+  time_mask_T: 100
+  time_mask_p: 1.0
+  time_wrap_W: 0
+transforms:
+  '*':
+  - utterance_cmvn
+  _train:
+  - utterance_cmvn
+  - specaugment
+vocab_filename: spm_unigram8000_st.txt

lang/gj/spm_unigram8000_st.model

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lang/hi/config_st.yaml

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+bpe_tokenizer:
+  bpe: sentencepiece
+  sentencepiece_model: ./spm_unigram8000_st.model
+input_channels: 1
+input_feat_per_channel: 80
+specaugment:
+  freq_mask_F: 27
+  freq_mask_N: 1
+  time_mask_N: 1
+  time_mask_T: 100
+  time_mask_p: 1.0
+  time_wrap_W: 0
+transforms:
+  '*':
+  - utterance_cmvn
+  _train:
+  - utterance_cmvn
+  - specaugment
+vocab_filename: spm_unigram8000_st.txt

lang/hi/spm_unigram8000_st.model

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lang/mt/config_st.yaml

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+bpe_tokenizer:
+  bpe: sentencepiece
+  sentencepiece_model: ./spm_unigram8000_st.model
+input_channels: 1
+input_feat_per_channel: 80
+specaugment:
+  freq_mask_F: 27
+  freq_mask_N: 1
+  time_mask_N: 1
+  time_mask_T: 100
+  time_mask_p: 1.0
+  time_wrap_W: 0
+transforms:
+  '*':
+  - utterance_cmvn
+  _train:
+  - utterance_cmvn
+  - specaugment
+vocab_filename: spm_unigram8000_st.txt

lang/mt/spm_unigram8000_st.model

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lang/ne/config_st.yaml

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+bpe_tokenizer:
+  bpe: sentencepiece
+  sentencepiece_model: /home/deepakprasad/nlp_code/Nepali_MUSTC/en-ne/spm_unigram8000_st.model
+input_channels: 1
+input_feat_per_channel: 80
+specaugment:
+  freq_mask_F: 27
+  freq_mask_N: 1
+  time_mask_N: 1
+  time_mask_T: 100
+  time_mask_p: 1.0
+  time_wrap_W: 0
+transforms:
+  '*':
+  - utterance_cmvn
+  _train:
+  - utterance_cmvn
+  - specaugment
+vocab_filename: spm_unigram8000_st.txt

lang/ne/spm_unigram8000_st.model

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lang/tm/config_st.yaml

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+bpe_tokenizer:
+  bpe: sentencepiece
+  sentencepiece_model: ./spm_unigram8000_st.model
+input_channels: 1
+input_feat_per_channel: 80
+specaugment:
+  freq_mask_F: 27
+  freq_mask_N: 1
+  time_mask_N: 1
+  time_mask_T: 100
+  time_mask_p: 1.0
+  time_wrap_W: 0
+transforms:
+  '*':
+  - utterance_cmvn
+  _train:
+  - utterance_cmvn
+  - specaugment
+vocab_filename: spm_unigram8000_st.txt

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